Human Factors in Design, Engineering, and Computing

AHFE International

Accelerating Open Access Science in Human Factors Engineering and Human-Centered Computing

Human Factors in Design, Engineering, and Computing

book-cover

Editors: Tareq Ahram, Waldemar Karwowski, Jay Kalra

Topics: Artificial Intelligence & Computing, Human Systems Interaction

Publication Date: 2025

ISBN: 978-1-964867-75-5

DOI: 10.54941/ahfe1006811

Articles

Warnings and Multilingual Audiences

Available approaches for communicating warnings and safety information to linguistically diverse audiences have advantages and limitations, but research on this topic is limited. We conducted a semi-systematic literature search to identify peer-reviewed scientific articles addressing communication of product safety information to consumers from diverse language backgrounds. Our review highlights three communication approaches: single-language, bilingual or multilingual, and non-linguistic. Existing research regarding advantages and limitations of each approach is summarized, along with opportunities for future research.

Leigh Grant, Yu Min Chung, Julia Diebol, Peter Shlanta, Iiona Scully

Conference Proceedings

EAT Da Vinci 3.0_Translating Cinematic Narrative into Media Art Installation

Transforming a cinematic narrative into an interactive media art installation requires more than aesthetic adaptation; it demands a structural rethinking of how stories are embodied, perceived, and enacted. EAT Da Vinci 3.0 reinterprets Ang Lee’s film, Eat Drink Man Woman (1994) as a participatory culinary experience, focusing on the film’s emotional themes—familial tension, emotional restraint, and symbolic gesture—rather than its plot or characters. This paper outlines the theoretical underpinnings, design strategies, and interactive components of the installation, emphasizing how cinematic language is reconfigured through embodied interaction, spatial design, and multisensory engagement. By embedding performative interfaces within a rotating dining table and dynamic smart-glass environment, EAT Da Vinci 3.0 establishes a media art form that translates narrative into ritual practice, revealing the cultural and emotional dimensions of food as both medium and message.

Jiaming Day, Shun Wen Chao, Lien-cheng Wang, Yi Shan Lan, Yu-hsuan Lin

Conference Proceedings

From Manual to Automated: Enhancing Inclusivity in Foreign Language Education with Technology

In the 2020/21 school year, the city state of Bremen (Germany) became the first German state to equip all students with iPads, thereby creating a key prerequisite for the digital transformation of teaching. Digital platforms open up new opportunities for interaction in the classroom and new forms of learning. This paper reports on the exploratory “MediaMeetsDiversity@School” project that aimed to investigate the viability and effectiveness of using digital learning platforms to support more inclusive foreign language education. The focus was on creating learning experiences that adapt to the needs of students with special requirements, including reading and spelling difficulties (dyslexia), multilingual students with limited exposure to German at home, and neurodivergent learners. To address the challenge of creating inclusive and adaptive digital content for diverse learners, we reviewed potential uses of AI in content creation. Since no established framework existed for automation in this context, we adapted and extended the SAE levels of automation to structure our analysis. The resulting framework distinguishes six levels of automation, ranging from Level 0 (manual content creation) to Level 5 (fully automated, unsupervised content generation at run-time). For the scope of this study, we focused on design-time automation (Levels 0–3) to ensure consistent and comparable learner experiences. We used web technologies to implement the app prototype, ensuring broad platform compatibility, while also optimizing it for the iPads used in Bremen schools. The app prototype consists of adapted content for two of the aforementioned target groups. The tasks for the dyslexia target group consist of multisensory training in phoneme-grapheme correspondence, while the tasks for the multilingual target group focus on promoting logic and metalinguistics. Both types of tasks progress from a tutorial to simple tasks (awareness), then to medium tasks (application), and finally to complex tasks (deepening). The content was evaluated in eight school classes (grades 5 to 7) from seven schools in Bremen and Lower Saxony. After a brief introduction, students worked with the prototype during a 90-minute school session. They then responded to a 22-question feedback survey with both scaled and open-text answers.The results indicate a high level of acceptance and engagement among students, who found the tasks enjoyable and motivating. However, technical issues and usability challenges were also observed during task execution. Despite the limitations identified, the study demonstrated the potential of targeted, technology-supported learning environments to meet diverse learner needs in inclusive classrooms. Further research is required to assess long-term learning outcomes and to explore higher levels of automation in adaptive content generation.

Alex Ackermann, Volker Paelke, Benjamin Tannert, Oliver Kück

Conference Proceedings

The effect of multi-sensory physical experiences in daily emotional self-tracking service for emotion self-awareness

This study examines the impact of physical interaction on reminiscing by developing a system comprising a physically interactive product and a digital app that visualizes collected data. It compares their effectiveness with mood trackers lacking physical elements. Reflection enhances social connections, strengthens family ties, and improves mental health. Though diaries aid emotion regulation, modern users prefer the convenience of mood-tracking apps. The study suggests that incorporating physical interaction enriches the reminiscence experience. It introduces "Reblower," inspired by Korean hand air blowers, which activates through handle-turning, allows users to place emotion-representative objects inside, and visualizes input via a mobile app. User surveys indicate that memories linked to physical interaction are more impactful. This research aims to guide future reflective product designs.

Chajoong Kim, Jihyeon Kim

Conference Proceedings

Parametric generation based graphic design and spatial expression research

Parametric design is nowadays a development hotspot in the field of design and graphic computing design. However, due to its existence need to have a little mathematical theory foundation and computer graphics software design foundation threshold exists, affecting designers and other enthusiasts on the threshold as well as the cost of learning, in the Internet platform media can be called and learn relatively few reference materials; (2) Methods: In this paper, we construct a complete set of development tool prototypes that can be adjusted and modified in any part of the complete traceable process from graphic design to spatial structural expression; (3) Results: This study explores the role of parametric design tool prototypes in the workflow and refines the strengths and weaknesses of parametric design in the past, and summarizes the framework of the study on the production of graphic to spatial production workflow using parametric design de-velopment prototypes; (4) Conclusions: It also summarizes a research framework for de-veloping prototypes for graphic to spatial production using parametric design, providing design case studies and design methodology support for design tool innovation in the context of emerging technologies.

Xinyang Hu, Xingyu Zha, Haoyang Liu, Mengyao Wang, Anni Zha

Conference Proceedings

Gender Stereotypes in Video Gaming: Impacts of Anxiety Levels, Verbal Communication, and Performance

With the emerging demand for video games as entertainment, it is important to examine human behavior when designing and developing games. Past research has shown support that gender stereotypes imposed on female gamers are prevalent within this space. Another study showed that when female players perceive being in an all male performative environment, they are prone to stereotype threat which hinders their own performance. To continue the efforts of exploring the detrimental effects of this problem, a pilot study was devised using a 2x2 factorial within subject design with team compositions (male vs female) and atmosphere (negative vs positive interactions) as the independent variables. Ten female participants played a first-person-shooter game called Valorant by Riot Games across two consecutive days where they would play in both team compositions for gender. At the end of each game, participants took a questionnaire to self-rate their game experience, anxiety levels and desirability to communicate during gameplay. Additionally, researchers observed game performance, frequency of in-game communication and confederate engagement. Researchers hypothesized that participants who played in the all high ranking male team with negative interactions would communicate less frequently and have lower desirability to communicate, have worse game performance, less engagement with the confederates, higher levels of anxiety and overall worse game experience than other conditions. Statistical analyses revealed the atmosphere factor (positive vs negative) significantly influenced performance outcomes (e.g., kills and ranking) and subjective experiences (e.g., game experience, team interactions, feelings of respect, safety, disrespected, discouraged, and supported). However, there was no significant main effect for team composition and no significant interaction effect between team composition and atmosphere. Continued research of this problem space is crucial to ensure video games are inclusive and enjoyable for all.

Jensine Doan, ANIL KUMAR

Conference Proceedings

Exploring Usability And User-experience Metrics With A Novel AR App In The MASTERLY Project

The present study describes an initial user-experience (UX) evaluation of prototype augmented reality (AR) interface which interacts with a novel industrial human-robot collaborative system. Seventeen participants with varying levels of experience with AR systems at the University of Patras development site were guided through the system’s functions before completing a short manual assembly task directed by the AR system. Participants evaluated their experience via a questionnaire comprising standardised psychometrics (NASA TLX, UEQ, mCSE, SUS, and the Ten-Item Personality inventory or TiPi), while additional questions permitted free responses regarding trust in the system, utility, and user preferences. Two final items investigated aesthetic and functional aspects of the visual interface, and the overall ease of first-time usage. Using correlation, we examined expected consistencies across different UX metrics and a short-form personality inventory. Initial findings from the survey are reported on the overall state of the UX, and modifications to the survey for future use in the MASTERLY project’s other use-cases. Participants reported widely positive interactions, and their responses also provided suggestions well improvements to the final questionnaire for subsequent testing.

Christopher Burns, Sarah Fletcher, Apostolis Papavasileiou, Themis Anastasiou, George Michalos, Sotiris Makris

Conference Proceedings

Drawing Dialogues Between Generative AI and Children with Autism: A Qualitative Study on the Externalization of “Understanding”

Although individuals with autism may face challenges in social cognition, this does not imply a lack of capacity for making meaning or understanding emotions. Instead, it reflects a paradigmatic difference in the pathways of understanding, wherein their unique forms of expression diverge from the normative “modes of understanding” assumed by mainstream society (Grandin, 2006; Gilberts, 2006). Expressions situated within the “individual–local” quadrant can still embody a deep sense of structure and systemic meaning (Mottron et al., 2006). Therefore, it is essential to expand the definition of “understanding” and to construct a more inclusive and structurally responsive conceptual model.This study seeks to explore, through a qualitative lens, a central question: Can interaction with generative artificial intelligence (AI) in the context of drawing help make autistic children’s understanding more visible and amplified? It proposes that the understanding capacities of children with autism may be redefined through such mediated engagement. The primary aim is to uncover the types of responses, misalignments, misunderstandings, or unexpected outcomes that arise in generative AI systems during interactive creation.This study focuses on the role of generative AI in the drawing processes of children with autism, exploring its impact on the externalization of “understanding.” Through an interdisciplinary literature review, the research identifies three key dimensions of understanding: organizational constructiveness, social embeddedness, and sensory processing. Based on these dimensions, a comparative experiment was designed and conducted, contrasting traditional pen-and-paper drawing with interactive drawing involving generative AI.Multimodal data were collected from 15 autistic children, their teachers, and researchers. Drawing on teacher interviews and observer ratings, the study analyzes the modes of understanding exhibited by the participants and how these evolve during the drawing process. Findings suggest that generative AI can stimulate children's creative potential through low-intervention support, mediating guidance, or co-creative feedback. In specific contexts, it effectively enhances cognitive structures and expressive intent, allowing previously implicit forms of understanding to be visually externalized.The study also reveals two key paradoxes: first, the deeper a child’s understanding of a subject, the richer their mental imagery and narrative details, yet this increased cognitive load in selection and organization may lead to slower and less direct expression. Second, while AI assistance can help concretize vague ideas, it may also obscure children's unique non-verbal and non-visual modes of perception, resulting in overly uniform external representations.The theoretical significance of this study lies in proposing a multidimensional and paradigm-shifting model of “understanding” within the context of generative AI–autistic child interaction. Practically, it offers insights for educational interventions aimed at unlocking the creative potential of children with autism, and informs future design strategies for generative AI drawing tools tailored to special-needs users, calling for a more inclusive, diverse, and interactionally grounded understanding.

Ying Jiang, Yifang Gao, Linlin Wang, Lexinyu Huang, Mengkun Bi, Min Hua

Conference Proceedings

Human-Centered Design of Integrated Food Service Management Systems: Reducing Cognitive Load in Resource-Constrained Kitchen Operations

Small-scale food service operations face significant cognitive challenges in managing fragmented digital systems. This field study evaluated an integrated management platform designed using human-centered principles at a single restaurant over five weeks. Six staff members (tested two at a time across morning and evening shifts) served 612 customers, generating 967 transactions. The study compared two weeks of baseline operations using fragmented systems (Square POS, manual inventory, separate communication tools) against three weeks using the integrated platform. NASA-TLX measurements showed cognitive workload decreased from baseline (M=58.7, SD=10.2) to post-implementation (M=44.3, SD=7.8), a 24.5% reduction. System Usability Scale scored 85.8 (SD=7.4), indicating excellent usability. Operational improvements included 35% faster order processing (8.2 to 5.3 minutes), 65% faster payment completion (12.4 to 4.3 seconds), and 72% error reduction (12.1% to 3.4%). Customer satisfaction averaged 9.3/10 (n=147, 24% response rate). The system architecture demonstrates enterprise-grade capabilities (real-time communication, AI decision support, comprehensive monitoring) at small-business cost (~$240/month). Results suggest integrated human-centered systems can significantly reduce cognitive burden while improving operational efficiency in resource-constrained environments.

Saumil Patel, Rogith Naini

Conference Proceedings

The Design Futures Art-driven (DFA) Method: Structuring Art-Tech Collaboration for Sustainable Future of Food System

The contemporary food system is among the most environmentally damaging sectors, contributing significantly to greenhouse gas emissions and sustainability challenges. A fundamental transformation across production, consumption, and waste management is urgently needed to ensure both human and planetary wellbeing. While technology and corporate innovation play crucial roles, growing attention is being paid to artists for their capacity to foster reflection, imagination, and transformative thinking. However, structured methods to guide and scale Art–Tech collaboration remain limited (Schnugg & Song, 2020). The Horizon Europe MUSAE project addresses this gap through the MUSAE Factory Model, a structured approach for future-driven innovation based on the Design Futures Art-driven (DFA) method. Integrating Futures Thinking, Design Thinking, and Art Thinking, the DFA method enables artists, designers, and SMEs to explore preferable futures and develop prototypes using emerging technologies. This article presents results from two art–tech residencies under the theme of Food as Medicine. In the first, 12 artists created visionary scenarios reimagining food practices for human and planetary health. In the second, 11 artist–SME teams translated selected scenarios into future-oriented prototypes (TRL5). Starting from shared values rather than predefined problems fostered systemic perspectives, stakeholder alignment, and the translation of future visions into actionable innovations. Findings show that structured art–tech collaborations based on the DFA method can accelerate ethical and sustainable technological innovation by generating future scenarios that inspire industrial practices and fostering early alignment between SMEs and creatives through guided facilitation. Projects like SOIL, Growing Futures, BITZ demonstrate the DFA method’s ability to produce diverse results based on future visions while integrating ethical and emotional dimensions often absent from traditional R&D. Overall, the MUSAE Factory Model, rooted in the DFA method, illustrates how structured, futures-oriented collaborations between art and technology can align innovation with human values, sustainability goals, and cultural meaning.

Tatiana Efremenko, Carmen Bruno, Eva Monestier, Marita Canina

Conference Proceedings

Increasing importance of Instinct

Everybody knows that if we miss "Now". That moment never comes back. Our world is "It is Now or Never". But the current Industrial Society which was brought forth by the Industrial Revolution is "Production-centric". And we have been pursuing product performance (better function with less cost). But as Maslow pointed out, human needs shift from material satisfaction to emotional satisfaction. We, humans, would like to "Actualize ourselves". In English, living things are called "Creatures", because we create movement to survive. "Movement" is indispensable for living. But in the case of four legged animals, they cannot move freely because their movement is restricted due to the fact that their body center of gravity remains the same position from the ground. But in old time, anthropoids jumped to trees and found out that if we get free from the earth's gravity, we can move as we wish. But we, humans, were wiser. Instead of jumping from tree to tree, humans chose to walk on two legs and turn the other two to arms. Thus, humans could develop arms to hands and became able to create movement as we wish. And this enables us to "Feel" movement within ourselves. Among vertibrate, only humans and apes can understand the mirror image is "Self", because we can "Feel" our movement inside of us. In the case of Invertibrate, only "Octopuses" can identify "Self" in the mirror image. Octopuses are incredible. They can escape even from a screwed container. Thus, they are known as "Expert of Escape". Further, they teach us to learn from failures. They do not repeat the same mistake and succeed in escaping. We have a lot to learn from octopuses. Their "Situational Awareness" is excellent. Well, let us come back to humans. We should remember that "Emotion" and "Motivation" come from the same Latin "Movere". In fact, "Emotion" means "e=ex Motion", i.e. to move out. We perceive the environment and situation and motived to establish our own world in the Real World. "DEI (Diversity, Equity, Inclusion" is getting wide attention these days. As to Diversity and Equity, we can satisfy these needs with the Industrial Society. But as everybody knows, society shifts from one to another with time. And the current industrial society is getting close to its end and it is time to design and develop another society for the next generation. As the Industrial Society is getting close to its end, there are many issues emerging. The greatest problem is excessive consumption of energy. We are running out of energy. AI (Artificial Intelligence) is regarded as a potential solution, but we must remember AI consumes a lot of energy. Thus, it does not work. Then, how about Inclusion. This is promising. As Maslow pointed out, the final need of humans is to actualize "Self". We, humans, feel maximum happiness and the feeling of achievement when we do the job internally motivated and realize it in our own way. The current industry is "Technology-based". It is Science. But the maximum happiness and the feeling of achievement are obtained by "Engineering-way". We make efforts to make our dream come true. Humans can only dream about the future, because we developed a surprisingly flexible body mobility. However, tackling with "Now" is very challenging. Now changes every moment. In fact, the Real World changes continuously from moment to moment. Let us consider swimming. Water changes continuously. So we cannot apply mathematical approaches. We need to make the most of our own instinct. That is the only tool we have to challenge the new, unexperienced world. To cope with the dynamism, we can introduce Recurrent Neural Network, but it assigns weights between nodes automatically in a random way. So, it is a black box. But if we introduce Reservoir Computing, we can manipulate the output, so we can manage the system. But far greater advantage of introducing Reservoir Computing is it enables us to introduce micro technologies. So, we can make sensors and actuators extremely small, so that we can make them part of our body. It really works for human augmentation. Usually, sensors detect signals and actuators which can process these signals are mobilized. But micro technologies can make it possible to work them together at the same time. It is none other than Instinct. Thus, how we can utilize these additional Instincts is, in a way, "Inclusion". Even seniors can think about the future. They can work to make their dream come true. They can live for tomorrow. To achieve this goal, it is important to make the most of "Images". When we forgot someone's name, we can identify who it is by looking at images. The tool to support Instinct using Images is developed in this research. In short, it is deeply related to "Marketing". Marketing is to expand the market. In fact, expanding market is nothing other than expanding our world of "Self".

Shuichi Fukuda

Conference Proceedings

Bridging the Privacy Gap: Stakeholder Solutions to Support Transparent Data Management Practices in Digital Health Research

Digital health research increasingly relies on commercial products like wearable fitness trackers, mobile apps, and social media platforms. Incorporating these technologies into research requires acceptance of third-party privacy policies, which describe how companies manage participant data. These documents are often lengthy and complex, creating challenges for researchers and institutional review boards (IRBs) responsible for identifying potential data risks. Despite this, privacy policy review is not always part of research oversight, leaving a critical blind spot in risk assessment. This study identified stakeholder priorities for improving privacy policy communication in digital health research and co-designed solutions to address these challenges. Guided by the Double Diamond design framework, a four-hour in-person co-design workshop was conducted in March 2025 at UC San Diego with 25 participants representing three stakeholder groups: IRB members, researchers, and research participants. Eligible participants had prior experience with digital health research. The workshop explored the Fitbit privacy policy in the context of a fictional digital health study. Participants moved through the four phases of the Double Diamond—Discover, Define, Develop, and Deliver—engaging in activities such as privacy policy analysis, problem statement development, issue prioritization, solution brainstorming, and prototype creation. Data sources included individual workbook responses, group discussion notes, prioritization votes, and final prototype presentations. These were transcribed, labeled by stakeholder group, and analyzed using Anthropic’s Claude Sonnet 4.0 for AI-assisted thematic and sentiment analysis, verified by a researcher. Participants co-created six prototype solutions: A policy scoring app A personalized data risk profile app A gamified platform experience An interactive, closed-loop consent tool A multi-format dashboard showing risks and benefits A tool supporting communication between researchers and IRBs Low-fidelity mockups of each prototype were generated using Sora AI. Five of the six prototypes were aimed at improving communication with research participants, while one specifically addressed IRB workflows. Participants prioritized features such as simplification tools, interactive consent interfaces, granular user control, and third-party transparency mechanisms. Canva was used to further refine low-fidelity designs. The prototypes were organized into three thematic categories: (1) privacy policy learning tools, (2) a data preferences dashboard, and (3) third-party risk assessment features. Digital health technologies carry potential data risks not always captured in research oversight processes, particularly due to the inaccessibility of third-party privacy policies. This study demonstrates the value of engaging stakeholders to co-design communication tools that support informed consent and transparency. Participants emphasized the importance of interactive, personalized, and accessible platforms that clearly convey data management practices and third-party relationships. These co-designed solutions provide evidence-based guidance for improving privacy policy communication in digital health research.

Ramona Pindus, Daniela Vital, Brittany York, Woocheol Kim, Karandeep Singh, Camille Nebeker

Conference Proceedings

Human Performance in High-Reliability Transportation Systems: The Role of Cultural Intelligence

High-reliability transportation systems—such as commercial aviation, maritime operations, and high-speed rail—operate under unforgiving conditions where failure can result in catastrophic outcomes. In these settings, human performance remains a decisive factor in maintaining operational safety, efficiency, and resilience. While traditional human factors research has addressed cognitive workload, situational awareness, and crew resource management, emerging challenges in an increasingly globalized and multicultural workforce call for a deeper understanding of interpersonal and cross-cultural dynamics. This paper focuses on Cultural Intelligence (CQ) as a pivotal yet underutilized capability that significantly affects performance in high-reliability transportation systems.Cultural Intelligence is defined as the ability to function effectively in culturally diverse settings. It encompasses four key dimensions: cognitive (knowledge of cultures), metacognitive (awareness and strategy), motivational (drive and interest), and behavioral (adaptability in communication and actions). In multinational transportation environments—where crews, controllers, technicians, and managers often operate across national, linguistic, and cultural boundaries—CQ has profound implications for communication clarity, conflict resolution, trust-building, and collaborative problem-solving.Drawing from empirical studies and real-world observations in commercial aviation and maritime shipping, this research demonstrates how high-CQ individuals and teams outperform their lower-CQ counterparts in areas critical to safety and reliability. For instance, in high-stress situations such as weather diversions, system malfunctions, or emergency landings, culturally intelligent crew members exhibit more effective coordination, fewer misunderstandings, and quicker consensus-building. These advantages are particularly apparent in multi-crew settings where standard operating procedures intersect with deeply embedded cultural communication styles, hierarchies, and decision-making norms.A key finding of this research is the correlation between CQ and error mitigation. Miscommunications, often stemming from culturally mismatched expectations, have been root causes in many transportation incidents. Crews with high cultural intelligence demonstrate greater sensitivity to these discrepancies and proactively bridge communication gaps. Training and development of cultural intelligence are shown to be both feasible and impactful. The paper outlines strategies for integrating CQ into existing human factors training programs, such as scenario-based simulations, intercultural workshops, and targeted feedback mechanisms. These interventions not only enhance individual awareness but also promote a team-level shift toward adaptive and inclusive operational norms. Importantly, the research advocates for the institutionalization of CQ as a selection and evaluation criterion in leadership development, crew pairing, and team performance assessment.Moreover, as automation and artificial intelligence continue to transform transportation systems, human performance remains the fail-safe layer of defense against system breakdowns. In this evolving landscape, the human-machine interface will require not only technical fluency but also advanced social cognition skills—including cultural intelligence. CQ will be essential in ensuring that teams can flexibly collaborate across traditional and digital boundaries, balancing human judgment with machine precision.In conclusion, this paper positions Cultural Intelligence as a critical human factor in the design, operation, and sustainability of high-reliability transportation systems. By recognizing and developing CQ among personnel at all levels, organizations can unlock a powerful lever for improving safety outcomes, operational resilience, and team performance in multicultural settings.

Dimitrios Ziakkas, Konstantinos Pechlivanis, Debra Henneberry

Conference Proceedings

Cultural and Visual Determinants of Avatar-Based Impression: A Cross-National Study on Relaxing, Trustworthiness, and Emergency Suitability

As AI-generated avatars become more common in public communication, understanding how facial features shape impressions across cultures is crucial. This study investigated how geometric facial traits influence perceived trustworthiness, relaxing impression, and emergency suitability among Japanese and Chinese participants. A total of 288 participants evaluated 24 AI-generated faces selected through Principal Component Analysis based on eye and mouth-related features. Eye-related features acted as universal predictors, whereas smile-related features varied culturally. These findings highlight the need for culturally adaptive avatar design, especially in high-stakes contexts such as emergency communication.

Li Wen Zhang, Toshikazu Kato, Takashi Sakamoto, Toru Nakata

Conference Proceedings

Cross-Cultural Expectations from Self-Driving Cars

While the international adoption of Autonomous Vehicles (AVs) is imminent, cross-cultural user expectations remain poorly understood. In this study we utilized a survey with 57 questions prepared in English, German, and Spanish languages, distributed in the United States (n=52), Germany (n=64), and Panama (n=41), that asked 157 participants about their personal driving behaviors as well as their expectations from Self-Driving Cars (SDC). Several novel behavior and AI trust metrics are generated from the responses that show clear differences in expectations of autonomous technologies depending on the demographic sampled.

Steven Tolbert, Mehrdad Nojoumian

Conference Proceedings

Emotional Intelligence as a Neuroergonomic Buffer in High-Pressure Professional Environments

Technical and business professionals increasingly operate in cognitively demanding, high-pressure environments that require sustained attention, adaptive regulation, and effective collaboration. Professionals such as software engineers, product managers, and business leaders routinely face complex incidents, accelerated release cycles, and human–AI collaboration scenarios that place heavy demands on cognitive and emotional regulation. This paper presents a theoretical model integrating Emotional Intelligence (EI) and neuroergonomics to explain how emotional regulation influences neural efficiency and cognitive stability under stress. The framework positions EI as a neuroergonomic buffer, a moderating mechanism that mitigates the impact of stress and workload, measured through electroencephalography (EEG) workload indices. Drawing on human factors and organizational psychology, the model proposes that EI competencies, self-awareness, self-regulation, empathy, and relationship management, enable professionals to maintain neural stability and cognitive efficiency in high-pressure situations. The framework explains how Emotional Intelligence influences EEG based workload responses by enhancing emotional and stress regulation, promoting neural efficiency, and facilitating faster recovery after demanding tasks. The paper concludes by outlining implications for leadership development, human technology systems design, and future neuroergonomic research on emotional regulation and performance in complex professional environments. By bridging Emotional Intelligence and neuroergonomics, this framework provides a conceptual foundation for empirical studies and organizational applications that integrate emotional competence development with neurophysiological feedback to enhance human performance, resilience, and well-being in cognitively demanding work environments.

Ravneet Kaur

Conference Proceedings

Pairing Child Rights Impact Assessment (CRIA) with Playful Interaction Design: From Teaching to a Tool in Design Education

Play has the potential to act as a civic method that benefits democracy by operationalizing the core principles of Child Rights Impact Assessment (CRIA) in accessible, inclusive, and future-oriented ways. A tool developed in Finland as the result of a teaching innovation in playful and gameful design exemplifies how humanities students can translate interaction design into a participatory tool for children, advancing child rights in decision-making. Mirrored against the principles of CRIA, the tool presented in this study on design education demonstrates systematic assessment through rule-based play formats, ensures participation by lowering barriers via narrative role-play, and fosters transparency by making collective outcomes visible to all participants. Importantly, the analysis of the teaching innovation presented through a design educator’s perspective situates this work within the framework of the child’s right to play, as recognized in Article 31 of the United Nations Convention on the Rights of the Child (UNCRC, 1989) and clarified in General Comment No. 17.Keywords: Playful democracy, Child Rights Impact Assessment (CRIA), CRIA Tool; Children’s Right to Play, Interaction design in Humanities, Participatory governanceIntroductionThe right of the child to play is a cornerstone of international human rights law. Article 31 of the United Nations Convention on the Rights of the Child (UNCRC, 1989) affirms that every child has the right to “rest and leisure, to engage in play and recreational activities appropriate to the age of the child.” This right has been further clarified in General Comment No. 17 (UNCRC Committee, 2013), which emphasizes that play is a fundamental aspect of children’s wellbeing, learning, and participation in society. As Lester and Russell have argued, play enables children to “make sense of the world and their place in it.” At the same time, Ginsburg underscores its role in strengthening not only individual growth but also relationships and collective engagement. Play must be taken into account when listening to the views of children, and children have the right to express their opinions and influence decisions made in Finland, where the study at hand was undertaken. Despite this recognition, play often remains marginalized in governance processes. Policy frameworks such as “lapsivaikutusten arviointi” (LAVA), Finland’s national model of Child Rights Impact Assessment (CRIA), aim to systematize the consideration of children’s rights in law and policy. CRIA, or LAVA in Finland, refers to the systematic process of analyzing the potential effects of policies, legislation, or administrative decisions on children. In this context, play offers an underexplored but promising pathway. Play can lower barriers to participation, create inclusive and multimodal spaces for expression, and render decision-making processes tangible through narrative and material outcomes. By embedding democratic principles within rule-based structures integrated in playful tools, play enables children to experience participation as a lived practice rather than an abstract right. Moreover, play’s imaginative and future-oriented dimensions provide tools for anticipating the impacts of policies, resonating with LAVA’s emphasis on foresight. A concrete example of this potential is the “Teddy Council”, a playful tool developed in Finland as part of teaching in playful and gameful design. Emerging from this course in interaction design taught to humanities students, this tool uses teddy bear narratives, tokens, and playful voting structures to invite children into collective decision-making. In doing so, it transforms the bureaucratic logic of CRIA into embodied, accessible, and transparent forms of play. From a pedagogical perspective, it also illustrates how design literacy can be cultivated in the humanities to produce innovations that are both playful and policy-relevant.This presentation proposal focuses on how playful interaction design was utilized as part of teaching playful and gameful design to a group of humanities students in Finland, and how a tool for Child Rights Impact Assessment was developed to mirror the principles of LAVA, to provide an accessible, scalable, and transparent tool to meet the needs of the local municipality.The analysis of the teaching innovation that resulted in the creation of the CRIA tool highlighted several contributions of pairing CRIA with playful interaction design, including novelty, relevance, play as a method for participation, the important role of materiality in playful interaction design, inclusivity achieved through multimodality and embodied interaction, and the importance of expertise involved in the teaching dimension.

Katriina Heljakka

Conference Proceedings

Dilemma-Based Decision-Making in European Digital Innovation Hubs (EDIHs) Ecosystem’s: Insights into Innovation Leadership

Decision-making in complex innovation ecosystems, such as European Digital Innovation Hubs (EDIHs), involves navigating strategic and ethical dilemmas that challenge conventional leadership models. Despite EDIHs’ growing role in Europe’s digital transformation, limited research explores how leaders manage conflicting priorities in these multi-stakeholder environments. This study examines how dilemma-based decision-making shapes innovation leadership and dynamic capabilities in EDIHs.Grounded in Hampden-Turner’s (1990) dilemma theory and Teece’s (1997, 2007) dynamic capabilities framework, the study employed a qualitative survey with 20 responses. The survey assessed leadership models, adaptability, resilience, and strategic decision-making. Findings reveal that leaders frequently face tensions—such as balancing financial sustainability with ethical neutrality, or short-term KPIs with long-term ecosystem impact. Their ability to reconcile these dilemmas is vital for fostering innovation, trust, and resilience. Leadership in digital innovation ecosystems demands agility, foresight, and integrity (Gorelova et al., 2024; Tigre et al., 2025). Leaders must possess creativity, innovation, and an entrepreneurial mindset to guide dynamic ecosystems effectively (Taylor et al., 2025; Ercantan et al., 2024). Ethical dilemmas, as defined by Fernando (2009), involve choices between alternatives that affect both stakeholders and organizational competitiveness. In EDIHs, these dilemmas are amplified by the need to balance public trust, technological neutrality, and commercial viability. Leaders’ decisions are shaped by values, cultural context, and their ability to interpret dynamic environments, aligning with ethical leadership and moral sensitivity in complex systems (Arar & Saiti, 2022).The European Digital Innovation Hubs network, launched in 2023, supports SMEs and public sector organizations in adopting advanced digital technologies. Covering nearly 90% of European regions (168 EDIHs), the network offers services in AI, cybersecurity, and high-performance computing. By September 2024, over 200,000 participants had engaged in more than 5,000 events, through which over 18,000 services were delivered. The Digital Maturity Assessment Tool (DMAT) shows that companies follow a structured path toward digitalization, with strategy and human-centric approaches becoming increasingly important at higher maturity levels. After receiving support from EDIHs, 90% of companies improved their digital maturity scores (Carpentier et al., 2025).This study contributes to bridging leadership theory, innovation management, digital ecosystems, and entrepreneurship research. It addresses a critical gap in understanding how leaders in digital ecosystems navigate dilemmas and leverage dynamic capabilities to foster innovation, trust, and transformation. Hampden-Turner’s (1990) dilemma reconciliation model emphasizes integrating opposing perspectives rather than seeking compromises. This approach enables leaders to transform conflicting values into creative solutions that support both value creation and long-term sustainability. The findings suggest that dilemma-based decision-making is not a barrier but a catalyst for innovation leadership. Leaders who can reconcile tensions—between innovation and regulation, neutrality and commercial interest, or short-term metrics and long-term vision—are better equipped to build resilient ecosystems. These insights are particularly relevant for policymakers, ecosystem designers, and innovation leaders aiming to strengthen the role of EDIHs in Europe’s digital future.ReferencesArar, K., & Saiti, A. (2022). Ethical leadership in complex systems. Journal of Educational Administration, 60(3), 345–360.Carpentier, A., et al. (2025). Digital Maturity Assessment Tool: Impact of EDIHs on SME transformation. European Commission Report.Ercantan, A., et al. (2024). Entrepreneurial mindset in digital ecosystems. Technology Innovation Journal, 18(2), 112–129.Fernando, M. (2009). Ethical decision-making in organizations. Journal of Business Ethics, 90(3), 371–383.Gorelova, L., et al. (2024). Leadership agility in digital transformation. European Management Review, 21(1), 55–72.Hampden-Turner, C. (1990). Charting the Corporate Mind: From Dilemma to Strategy. Blackwell.Msila, V. (2024). Janusian thinking in leadership. Leadership and Change Quarterly, 12(1), 88–101.Taylor, R., et al. (2025). Innovation leadership in European ecosystems. Innovation & Strategy Review, 33(4), 205–223.Teece, D. J. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.Teece, D. J. (2007). Explicating dynamic capabilities. Strategic Management Journal, 28(13), 1319– 1350.Tigre, P., et al. (2025). Multi-stakeholder collaboration in digital hubs. Digital Policy Studies, 9(2), 144–160.

Pirita Ihamäki, Päivikki Kuoppakangas, Jari Kaivo-oja

Conference Proceedings

Standardizing human performance evaluation in MBSE

This review examines research on the standardization of human capability, efficiency, and performance in model-based systems engineering (MBSE). It addresses fragmentation and inconsistency for integrating human factors in MBSE. The literature review aims to evaluate the current integration of human metrics in MBSE, explore modeling methodologies, and validation approaches. It also identifies the pathway toward standardization. On the other hand, due to a lack of real-time data, validation is also difficult for this process. Under these circumstances, standardizing human factors will open a new era in this field. It will lead to better, more accurate system designs and improve communication among human-digital components in a complex system.

Faysal Ahmmed, Sean Walker

Conference Proceedings

AIToys: A Sustainability Approach

Artificial Intelligence (AI) has increasingly entered the domain of play, transforming the affordances and functions of toys. AI-empowered toys are becoming prominent partners in lifelong and life-wide play, enhancing entertainment, education, and the development of empathy. Simultaneously, sustainability is often overlooked as an aspect of toy innovation. This study examines four existing and one speculated example of AIToys on the market through the lens of sustainability, aiming to understand how the three pillars of sustainability—ecological, ethical, and economic—are addressed in manufacturers' claims. The data consists of toy-maker descriptions and media articles, which were analyzed. The study's contribution is to examine and describe how AIToy development aligns with sustainability principles, a currently relevant area of research prompted by the ongoing transformation related to how Artificial Intelligence impacts human-toy relations. This study investigates AIToys through the lens of sustainability, emphasizing ecological, ethical, and economic considerations in their design. Unlike earlier generations of smart or connected toys, AIToys integrate advanced AI features that support continuous engagement beyond childhood. By analyzing manufacturer descriptions and media coverage of a selected sample of AIToys available in the 2025 online market, this research identifies key design directions that align with sustainability principles. The concept of AIToys, Artificial Intelligence-powered toys, has emerged as a distinct category within the broader domain of smart and connected play technologies. According to Heljakka and Ihamäki (2025), an AIToy is defined as a play object that integrates artificial intelligence to enable machine learning, interactivity, adaptability, and autonomy. These features distinguish AIToys from traditional toys and earlier generations of smart toys, which typically rely on pre-programmed responses or basic connectivity. Key capabilities of AIToys include adaptive learning, which ability to evolve behavior based on user interaction. Second one is natural language interaction, which is engaging users thought meaningful conversation. Third is emotional responsiveness, its recognizing and responding to affective cues. Fourth is physical motion embodied interaction through robotics or sensor-based movement. Aguilera et al. (2024) observe that contemporary AI-enhanced toys and robotic systems designed for children incorporate varying levels of artificial intelligence technologies—including machine learning, deep learning, natural language processing, and computer vision. These capabilities enable advanced functionalities such as facial and emotional recognition, voice-command-driven actions, personalized learning pathways, and support for multilingual interaction. In addition to the integration of AI systems, Costa, Périno and Ray-Kaeser (2018) emphasize the importance of the toy’s physical attributes in shaping the user experience. These include the presence of functional components such as cameras, speakers, and supplementary accessories designed to enhance play. Furthermore, the toys are characterized by their visual and tactile qualities—such as color schemes, textures, dimensions, and weight—as well as their capacity for movement and expressive gestures. While technological innovation is central to AIToy design, sustainability—ecological, ethical, and economic—is often underrepresented. At the same time, the importance of sustainability in toy design has grown significantly, especially in alignment with the United Nations Sustainable Development Goals1 (SDG 12, SDG 9, SDG 3). As Yadou et al. (2025) emphasize, toy design is not merely aesthetic or functional—it has a profound impact on child development, the environment, and society. Their study highlights that most toys on the market are plastic-based, short-lived, and environmentally harmful. Sustainable toy design requires the use of safe, biodegradable, and recyclable materials, as well as eco-friendly manufacturing processes.The research suggests that sustainable toy design must adopt a holistic approach, considering, user needs and developmental stages, material safety and environmental impact, energy-efficient production and waste management and product lifecycle and circular economy principles. For example, LEGO and Mattel are transitioning to bio-based and recycled plastics, while Hasbro has launched a toy recycling program. Additionally, 3D printing and modular design enable material-efficient and multifunctional toy production. Beyond the materiality of AIToys, questions arise about their longevity as interactive and relational technologies, which propose challenges regarding, e.g., e-waste and the socio-emotional attachment to non-human entities. This paper highlights these concerns through the lens of sustainability.

Pirita Ihamäki, Katriina Heljakka

Conference Proceedings

Enabling Data-Driven Collaboration: Leadership, Culture, and Knowledge Management in the Digital Enterprise

As organizations increasingly orient themselves toward data-driven goals, the transformation into data-driven organizations (DDOs) has become a strategic imperative for achieving sustainable competitive advantage through improved decision-making, accelerated innovation, and enhanced operational efficiency. Central to this transformation is the establishment of a data-driven culture (DDC), which functions as a critical enabler for unlocking the full potential of data as a strategic organizational asset.This paper examines the interplay between leadership, organizational culture, and knowledge management as key drivers in building a robust DDC. While the declining costs of data collection, storage, and processing have amplified the availability and strategic value of data (Lemmon & Lemmon, 2013), the real challenge lies in effectively embedding data-driven thinking into the fabric of the organization. Modern enterprises are increasingly investing in initiatives that span the development of scalable data architectures, the deployment of advanced analytics capabilities, and the democratization of data access across functional and hierarchical boundaries (Awasthi & George, 2020; Schmidt et al., 2023). These efforts aim to break down organizational silos, promote cross-functional collaboration, and foster a culture of shared data ownership and learning.However, the success of such initiatives is not solely contingent on technology. It hinges on leadership’s ability to steer cultural change, incentivize collaboration, and establish effective structures for knowledge sharing and upskilling. As artificial intelligence and advanced analytics become embedded into everyday business processes, continuous learning and knowledge management become essential for keeping pace with technological advancements and for building organizational resilience. In this context, the DDC is best understood as a socio-technical construct—one that requires alignment between technological capabilities, leadership behavior, cultural norms, and knowledge practices.This study adopts a socio-technical perspective to investigate how leadership influences the formation and maturation of a data-driven culture. By integrating insights from interdisciplinary literature—spanning leadership theory, organizational culture, knowledge management, and information systems research (e.g., Schmidt et al., 2023; Barbala et al., 2024)—with qualitative findings from semi-structured expert interviews conducted within a German multinational enterprise, the paper identifies both enabling factors and barriers to DDC implementation. Particular attention is paid to leadership competencies, mechanisms for cultural alignment, and the role of collaborative knowledge processes.The findings demonstrate that effective leadership, in combination with an adaptive organizational culture and structured knowledge-sharing practices, can significantly enhance the success of data-driven initiatives. This research contributes to the evolving body of knowledge on data-driven transformation by providing a conceptual and empirical basis for understanding the socio-technical dynamics of DDC implementation. It also offers practical implications for leaders and organizations seeking to operationalize data-driven strategies and leverage data as a source of long-term competitive advantage.

Alexander Nedelchev, Vinzenz Krause, Stefan Niggl, Sebastian Smerat

Conference Proceedings

Interactive Digital Narrative for Cultural Heritage: Game Design Taking Night Revels of Han Xizai as a Case Study

In the context of global digital transformation, traditional cultural dissemination is shifting from one-way linear transmission to interactive communication. Video games, with immersion, interactivity, and multi-dimensional storytelling, are emerging as innovative carriers for cultural heritage. However, digitizing traditional painting-based heritage still faces two key challenges: reliance on linear, pre-scripted narratives that limit exploration, and one-way knowledge transfer that results in fragmented historical understanding.To address this, the study transforms The Night Banquet of Han Xizai into a narrative-driven strategy puzzle game. Drawing on Ryan’s three-layer model of interactive narrative and Chatman’s narrative structure theory, it analyzes the painting’s narrative elements and proposes a game design framework aimed at reducing players’cognitive load and encouraging active cultural inquiry. A System Usability Scale (SUS) evaluation with 12 participants showed that combining interactive narrative with card-based strategy improved both usability and learnability.

Yingxi Xu, Yinying Tao, Chaohong Ding, Luqian Zheng, Chenge Wang

Conference Proceedings

Formatting Usability Guidance For Agentic AI: How Structure Shapes First‑Pass Quality

Agentic AI (AAI) systems increasingly act, not just answer. When teams use AAI to scaffold software creation, the format of the usability standards and design guidelines they feed into these systems may determine whether the first pass is usable or needs heavy rework. Yet most guidance focuses on what to design, not how to format guidance for AI consumption. Objective. Evaluate how different formats of usability standards/design guidelines affect AAI first‑pass outputs in application‑development contexts.Method. We authored four distinct guidance styles (varying in structure, constraint explicitness, and exemplar density) and applied each across two AAI/GenAI application‑development services executing the same feature‑level build tasks. Blind outputs were rated by an expert panel of three Human Factors/UX specialists against anchored criteria for task fit, constraint adherence, and revision effort. Ratings were aggregated and compared across styles and platforms; inter‑rater agreement and cross‑platform consistency were examined. Normative heuristics (e.g., Nielsen’s 10) informed rubric construction and our emphasis on compact, chunked, example‑rich inputs. Results & Contributions. The study isolates formatting as a lever for first‑pass adequacy in AAI development. We report which style(s) yielded higher expert ratings across platforms, summarize inter‑rater agreement, and distill formatting patterns (ordering, constraint statements, examples, acceptance checks) that traveled well across tools. We provide a reusable “meta‑spec” for authoring AI‑ready usability guidance that aligns HCD, HAI guidelines, and risk controls while preserving designer intent. Implications. For product teams adopting AAI, small changes in how standards are formatted can reduce rework, speed iteration, and connect design practice to measurable outcomes long associated with high‑maturity design organizations.Poster takeaways (practitioner‑ready):1. Treat usability guidance as input UX: structure, explicit constraints, and worked examples are first‑class variables—measure them.2. Author guidance to an AI‑ready “meta‑spec” (sections, acceptance checks, examples, and known failure modes) to improve first‑pass adequacy across tools.3. Build rubrics from established HCD/HAI sources; use expert review for quality and agreement checks before scaling.4. Close the loop: track rework hours saved to connect formatting choices to lean delivery and design ROI.

Michael Jenkins, Caroline Kingsley, Craig Johnson, Laura Mieses

Conference Proceedings

Logic-Based Inference for Automated compliance checking on indoor accessibility requirements

Compliance checking for building designs based on Americans with Disabilities Act (ADA) requirements is mandatory to support accessibility. However, it is now mainly conducted manually and thus time-consuming and labour-intensive. Research has developed methods to support automated compliance checking (ACC) for building codes in separate phases. However, few of the existing work explored accessibility requirements. Moreover, they focused on individual phases without integrating them together to achieve ACC. In this research, we proposed a framework to achieve ACC for one requirement in ADA regarding accessible 180-degree turns. The framework first finds a path between any two elements in Building Information Modelling (BIM); after that it identifies if there are any 180-degree turns; and lastly the compliance checking outcome for the turn will be determined. In our experiment with 96 cases, the algorithm successfully identifies 180-degree turns and outputs compliance checking outcomes.

Jingyi Lai, Jiansong Zhang, Bradley Duerstock, Nan Kong

Conference Proceedings

The Design challenges of Finding Engineering Solutions for the Ingress and Egress of disabled Passengers into Small sub-Regional Aeroplanes

Examples of design solutions for the ingress and egress of wheelchair users into large and small passenger airplanes already exist on the market. However, they are predominantly ground-based, expensive, single-aircraft solutions. The Innovate UK Hydrogen Electric and Automated Regional Transportation (HEART) project addressed, as part of its programme, the design challenge of enabling wheelchair users to travel on smaller planes such as those used on sub-regional routes with 19 seats or fewer. The approach taken was systematic user-centred Inclusive Design methodology based on functional, analytical capability analysis (Langdon 2010) and an end-to-end design method exploring a design space of alternative technical and ergonomic design solutions, for implementation within an envelope of requirements (Stanton et al. 2021). The objective was to identify and rank design alternatives for assisting ingress and egress of small aeroplanes by passengers with physical capability ranges that led them to use a wheelchair.For this to succeed it was essential that the developed designs should align with the needs, expectations, and lived experiences of its intended users. The design process was therefore based around disabled users and their experience. The project prioritised user-centred design, ensuring that proposed solutions were not only technically feasible but also practically usable by single operators. The study engaged disabled collaborators, and key industry stakeholders. The design process commenced with a review of literature and current regulatory environments, and a benchmarking of market offerings for a range of aircraft and handling equipment. This was followed by a technical requirements analysis where key limits included anthropometrics, size, weight, de-mountability, single person usability, portability, and the necessity to carry inside the aircraft and be self-powered. Building on these insights, a design workshop based on Stanton et al. (2021), brought together diverse stakeholders, including an aircraft engineer, designers, a CAA regulator and wheelchair users. Participants engaged in a creative exercise to generate and then technically evaluate convergent solutions, which were documented in concept sheets. These concepts were then ranked using a Pugh Matrix scoring system and an inclusion audit assessing usability across vision, physical, hearing, thinking, and stamina categories.The paper presents the key findings at each stage focusing on the accessibility challenges in small aircraft. Key technical issues identified included: restricted space; vertical movement, such as reaching and descending from the aircraft door level, and rotational and horizontal movement required for passengers to enter the aeroplane and secure themselves as regulated.The trials with the manufactured prototype were successful in engineering terms and received positively by collaborating disabled users, suggesting that the operation of the lifting equipment was potentially beneficial for disabled passengers if scaled up to airline service. Further design work will examine variants for similar aircraft and conduct inclusive disability trials.Stanton, N.A., Revell K.M.A., and Langdon, P., (2021), Designing Interaction and Interfaces for Automated Vehicles: User-Centred Ecological Design and Testing. 2021: CRC.

Pat Langdon, Roshan Dhonju

Conference Proceedings

Development and Integration of a Wearable Biometric Sensor Suite for Assessing Physical and Cognitive State

This work describes how a human-centered design process was implemented for development of a system of wearable technology that can be used to assess a person’s physical and/or cognitive state. The system is comprised of a suite of biological measurement devices. Variables measured include numerous heart rate variables, temperature, respiration, oxygen saturation and numerous brain wave patterns. Some, but not all, of the sensors used in the system are commercial off the shelf (COTS) sensors. Two sensos in particular were developed in house and have distinctive capabilities not found in COTS sensors. The first of these non-COTS sensors is very small and has the capability to deliver oxygen saturation and full waveform heart rate data. The full heart rate data allows the system to track an individual’s heart rate characteristics over time and use that individual’s data to set markers that may be correlated to changes in physical or cognitive state. The second of these non-COTS sensors is a head band that has the capability to measure certain brain wave patterns. The suite of sensors is integrated into a combination of a wearable athletic shirt and the head band. Each sensor has been calibrated and shown to have good accuracy and precision. The system’s different biological variables are displayed via an app that was developed as part of the research. The person wearing the sensor suite can use the app to view different biological data at any point in time and compare that data with their individual averages. Common measures of distribution of the biological data such as maximums, minimums and standard deviation are available and can provide insights into current physical and cognitive state. The sensor suite has been found to be relatively comfortable to wear. Initial tests validate that the sensors react in a predictable manner to external inputs intended to create heightened levels of fear or anxiety. Initial hypotheses have been developed for correlating the variety of data gathered by the system with an individual’s physical and mental well-being. The system can be used in the future to develop data algorithms that are individualized to the particular wearer, taking the different sensor outputs and correlating them with physical and cognitive states to inform users and possibly even recommend health improvement strategies. The work was sponsored and co-lead by the Air Force Research Lab. The work was accomplished as part of a design project embedded in the Westmont College engineering program.

Daniel Jensen, Lindsey Mcintire, John McIntire, Maya Pablos, Todd Knight, Richie Hibbs, Cameron Wilcox, Josh Wozniak, Caleb Jensen

Conference Proceedings

Smart Backpack System: A Wearable Educational Technology for Enhancing Student Organization and Academic Performance

Educational challenges in underdeveloped regions are often worsened by limited access to organizational tools and technological resources, making it hard for students to manage their academic responsibilities effectively. This study presents the Smart Backpack System, a fully developed wearable solution designed to improve student organization, encourage academic discipline, and support daily learning through integrated technology.The system was created through three iterative design phases. The initial RFID-based prototype aimed to reduce forgetfulness but faced issues with multi-tag detection and feedback. A second version introduced more reliable RFID, ergonomic improvements, and touch-based interaction. The final prototype included a detachable digital display, dual power sources (solar and rechargeable battery), and software applications for homework reminders, personal schedules, gamified learning, and item-tracking features.Evaluation involved 40 Somali students from elementary, middle, and high school levels, supported by four teachers. Mixed methods, including hands-on trials, structured surveys, and interviews, were used. Results were very promising: 95.7% of students said they would recommend the backpack, while 85% reported that notifications helped them avoid forgetting important items. Younger students appreciated reminder features and visual cues, while older students preferred customization options that matched their existing digital routines. Teachers noticed improvements in preparedness, punctuality, and responsibility, noting the backpack’s potential to reduce missed assignments and encourage proactive learning habits.This study shows how user-centered, technology-enhanced design can meet real-world educational needs. The Smart Backpack provides a scalable, culturally adaptable tool that can boost academic performance in resource-limited environments, while also setting an example for wearable educational technologies that smoothly integrate into daily student life.

Abdiaziz Omar Hassan, Cheng Yao, Peter Walusimbi, Saandi Youssouf, Amir Ubed, Said Youssouf Kadafi

Conference Proceedings

Identifying Approaches to an Accessible Society for Persons with Disabilities Through an Eight-Country Comparison

Japan’s 1993 New Long-Term Programme introduced four accessibility barriers—physical, system, information/culture, and psychological—which have guided policy for more than three decades. This paper reassesses this framework through an international comparison with seven countries (U.K., Finland, Australia, U.S., Thailand, India, and Vietnam), focusing on mobility and daily movement. Information obtained from semi-structured interviews with resource persons in each country and insights from Japanese experts were integrated to identify common themes and differences.Findings indicate that while no other government has a classification like Japan’s, common themes exist across several contexts, notably human rights, implementation/enforcement, and finance/affordability. Japan’s strongest advances lie in physical accessibility, but the rights perspective and practical implementation still need to be strengthened. This paper recommends retaining the four-barrier framework while adding human rights as a foundational lens and incorporating implementation/execution and finance/affordability as cross-cutting indices for measurable improvement.

Yoshito Dobashi

Conference Proceedings

Creating Shared Value in Platform Economies: The Social and Ethical Role of Ride-Hailing Platforms

The rapid evolution of platform businesses in the digital economy has transformed traditional industries, leading to significant shifts in how value is created and distributed. This research explores the development of a new Creating Shared Value (CSV) theory tailored to the unique characteristics of platform businesses, with a focus on the ride‑hailing sector. While ride-hailing platforms have achieved substantial economic growth, their societal impact remains controversial, with concerns over labor practices, regulatory challenges, and environmental implications. This paper argues that a revised CSV theory is essential to guide platform businesses in balancing economic objectives with social responsibilities by integrating the dynamics of multi‑sided platforms, network effects, stakeholder engagement and changing people’s behaviour to improve social norms. A case study of the ride‑hailing service Careem is selected to observe the actual process and actions taken. The proposed theory emphasises the importance of co‑creating value with all stakeholders, including drivers, passengers, regulators and communities, to achieve sustainable business growth and societal benefits. Data from interviews, direct observation and publicly available company information were analysed through an explanation‑building technique. The findings contribute to theory and practice by offering a CSV framework adapted to platform business contexts and providing managerial and policy insights into how platform firms can promote positive social behaviour.

Amna Javed, Youji Kohda

Conference Proceedings

A human-centered approach to support business process analysis

Business process analysis constitutes an essential prerequisite for business process optimization. Identifying problematic areas in a process and defining the optimization goals are important aspects of process analysis. This paper proposes a novel approach to process analysis that is suitable for both experts and novices. The novelty of the approach lies in the definition of a set of distinct, generic goals based on usability criteria, as well as the classification of problems that can occur in a process. The developed approach supports the identification and definition of problems concerning a business process, as well as the formulation of precise optimization goals. This in turn facilitates the finding of effective solutions and the achievement of goals.

Daniel Feiser, Elena Dalinger, Nina Mundt

Conference Proceedings

Enhancing the Accessibility and Comprehension of Online Informational Text: An ASL Sentence Structure Approach

Deaf individuals with limited English proficiency often face barriers accessing online text due to linguistic differences between English and American Sign Language (ASL). This study investigates whether presenting health information using ASL sentence structure can enhance comprehension, usability, and user experience. In a controlled between-subjects experiment, ten deaf adults were randomly assigned to view university health information either in traditional English or in ASL-structured text. Quantitative and qualitative analyses revealed that participants in the ASL group achieved substantially higher comprehension (M = 81%) than those in the English group (M = 29%), completed tasks faster (9.4 min vs. 29.2 min), and made fewer errors (0.2 vs. 1.8). User satisfaction was also higher in the ASL group (80% vs. 0%). Thematic analysis identified four recurring benefits, visualization, support, comprehension, and accessibility. These demonstrate that aligning written content with ASL grammar improves both understanding and engagement. These findings extend Text Simplification (TS) research by showing that linguistic adaptation grounded in ASL structure can bridge comprehension gaps, supporting more inclusive and equitable digital communication for the Deaf community.

Olarinde Farayola, Dastyni Loksa

Conference Proceedings

Design of Camping Bathing System Based on Kano Modeling

With the growing popularity of outdoor activities and nature appreciation in recent years, the camping economy is experiencing unprecedented growth. This trend not only drives the rapid expansion of the camping products market, but also demands higher standards for the design and quality of camping products. To meet the diverse needs of camping users, this paper adopts a research method based on the Kano model to delve deeply into the real needs and experiences of camping users. In terms of research methods, this paper combines literature research, web crawler, sentiment analysis, and Kano questionnaire survey to deeply explore the real needs and experiences of camping users, and takes showering facilities as an example to propose design strategies for camping showering facilities. The research findings of this paper are of great significance for enhancing the design quality of camping products, meeting user needs, and promoting the healthy development of the camping economy. By understanding user needs deeply and enhancing product design quality, we can better meet user expectations and needs, enhance user satisfaction and loyalty. At the same time, it also helps to drive the sustainable development of the camping economy and inject new vitality and impetus into the camping industry.

Jing Peng, Meiyu Zhou

Conference Proceedings

Inclusive Design Strategies for Neurodiverse University Learning Environments: Developing a Practical Toolkit

This research explores the development of an inclusive design toolkit aimed at creating supportive university learning environments for neurodiverse students, particularly those with ADHD. Traditional educational spaces often fail to address the unique sensory, spatial, and emotional needs of these individuals, which can lead to overstimulation, anxiety, and difficulties with focus and navigation.The study was conducted both at desk and in the field. To gain a comprehensive understanding of the topic, a thorough literature review was carried out, focusing on how the environment can affect people with ADHD. This was complemented by direct user involvement, including surveys and observations of a sample of ADHD students in their natural learning spaces.By synthesizing the literature, user research results, and case studies, this study identifies key design principles such as sensory regulation, predictability, flexibility, and comfort. It also analyzes important environmental elements—including lighting, acoustics, color schemes, furniture, spatial zoning, wayfinding, and technology integration—to propose practical strategies that reduce sensory overload and improve accessibility.A practical checklist is provided to help architects, interior designers, and planners implement these principles in a cost-effective way. This toolkit aims to bridge the gap between theory and practice, fostering equitable, calm, and adaptable learning spaces that support both academic success and well-being for neurodiverse students. The effectiveness of the design criteria included in the toolkit has been analyzed with regard to autism, sensory processing differences, and other cognitive variations.Finally, the toolkit was applied in a case study where the interior of a university classroom was redesigned to better meet the specific needs of ADHD students.References:1. Wender, P.H., L.E. Wolf, and J. Wasserstein, Adults with ADHD. An overview. Ann N Y Acad Sci, 2001. 931: p. 1–16.2. Doyle, A., et al., What does an ADHD-friendly university look like? A case study from Ireland. International Journal of Educational Research Open, 2024. 7: p. 100345.3. Coburn, A., et al., Psychological and neural responses to architectural interiors. Cortex, 2020. 126: p. 217–241.4. Finnigan, K.A., Sensory responsive environments: A qualitative study on perceived relationships between outdoor built environments and sensory sensitivities. Land, 2024. 13(5): p. 636.5. Alqahtani, L.A., Furnishing and Indoor Environment for Hyperactivity and Distracted Attention (in the Context of Sustainable Design). NEW ARCH-INTERNATIONAL JOURNAL OF CONTEMPORARY ARCHITECTURE, 2015. 2(1): p. 1–10.6. Saloni Kansal, D.D.P.R., and Pooja Singh, Inclusive Interiors for Neurodiverse Students. International Journal of Sustainable Building Technology, 2024; 07(02): p. 44–59.7. Narenthiran, O.P., J. Torero, and M. Woodrow, Inclusive design of workspaces: Mixed methods approach to understanding users. Sustainability, 2022. 14(6): p. 3337.

Attaianese Erminia, Elmira Bohlouli, Amirhossein Rezazadeh, Viviana Saitto, Morena Barila

Conference Proceedings

Beyond Population Decline: A Dual Tracking Framework of Smartphone GPS and Resident Registry for Disaster Recovery in Coastal Japan

This study examines post-disaster population dynamics in Wajima City, Ishikawa Prefecture, following the 2024 Noto Peninsula Earthquake. By comparing smartphone-based GPS mobility data and resident migration data from the Basic Resident Registry, we identify a divergence between visitor inflow and sustained resident outflow. A dual tracking approach is introduced, visualizing both trends and their interaction using system thinking and causal loop diagrams (CLDs). Our analysis reveals structural gaps between surface-level recovery and long-term demographic decline. This framework offers a transferable methodology for assessing disaster recovery in coastal regions worldwide.

Yasushi Takenaga

Conference Proceedings

Risk Analysis for optimizing cleaning processes in material transfer systems: Reducing cross-contamination in port operations

Port operations that simultaneously handle clinker, coal, and grains face complex challenges associated with cross-contamination. These issues directly affect operational efficiency, worker safety, and compliance with environmental regulations. Although advances in conveyor technology have improved material handling, there is still limited understanding of how cleaning processes mitigate contamination risks. Methods: This study applies a comprehensive qualitative risk analysis of cleaning systems in conveyor belts and hoppers, with emphasis on design and operational conditions that minimize contamination. Expert knowledge was gathered using HAZOP and SCAMPER techniques. Hazards were systematically evaluated through Fault Tree Analysis (FTA), Event Tree Analysis (ETA), and Bow Tie modeling. These methods allowed a structured identification of hazards, risk factors, and the effectiveness of preventive and mitigation barriers. Results: The analysis identified 27 design conditions (e.g., nozzle positioning, belt scraper optimization) and 21 operational conditions (e.g., cleaning frequency, inspection protocols, operator training) that contribute to reducing contamination. Failures in cleaning systems, conveyor operations, and dust collection were found to be key risk factors. A total of 34 preventive barriers, including high-pressure nozzles, automated washing systems, and pressurized air mechanisms, and 14 mitigation measures, such as vacuum trucks and dockside cleaning protocols, were assessed. Incorporating human factors into the risk framework underscored the role of operator awareness and structured decision-making in enhancing system reliability. Conclusions: The results demonstrate that integrating preventive and mitigation barriers significantly lowers the likelihood of cross-contamination events, strengthening both operational safety and environmental performance. This research provides practical guidance for port authorities and operators to optimize cleaning strategies, reduce material loss, and ensure regulatory compliance. Furthermore, it lays the groundwork for future quantitative risk analysis and highlights the potential of advanced technologies and automation to further improve cleaning effectiveness. By bridging a critical knowledge gap, this study supports safer, more efficient, and environmentally sustainable port operations. The insights presented are valuable for stakeholders across the maritime logistics chain who seek to balance productivity with environmental responsibility.

Rodrigo Dominguez, Evelyn Alfaro, Carlos Gomez, Francisco Ortiz

Conference Proceedings

Inattentional Blindness in Assembly Tasks: Implications of Cognitive and Perceptual Load for Human-Centered Interface Design

The inability to perceive a visible but unexpected stimulus is a phenomenon studied in cognitive psychology under the name of inattentional blindness (IB). IB reflects the limited capacity of human attention, as a demanding task may leave few, if any, attentional resources available for an unexpected yet visible stimulus to capture attention. The main cognitive mechanism involved is selective attention, which allows individuals to prioritize information relevant to the task at hand while filtering out irrelevant elements. However, depending on the context, some information may be mistakenly considered as distracting, particularly when it is unexpected. According to load theory, the efficiency of selective attention depends on the level of cognitive and perceptual load during task execution. To date, IB has been primarily explained through the lens of perceptual load, with cognitive load often reduced to executive control tasks. Moreover, the interaction between these two types of load remains underexplored, as does the investigation of IB in applied contexts relevant to everyday activities. To address these gaps, the present study aims to identify the occurrence of IB during manual assembly tasks in industrial settings. A 2x2 within-subject experimental design (N=32) was implemented, independently manipulating perceptual and cognitive load. Participants performed simulated assembly tasks using LEGO components. The main dependent variable was the detection rate of an unexpected stimulus, which was briefly presented on the instruction screen. Results show a significantly higher overall rate of non-detection compared to the control condition. In addition, a high level of either cognitive or perceptual load significantly increased the rate of non-detection. A strong interaction effect between the two types of load was also observed. These findings highlight the importance of considering both cognitive and perceptual load in the design of human-machine interfaces (HMIs), especially when information is delivered dynamically. Inattentional blindness, as a cognitive bias, can affect any individual particularly in high-load environments such as assembly procedures. Modern HMIs increasingly rely on real-time adaptability and contextualized alerts. However, their effectiveness depends on the timely availability of attentional resources, which this study shows to be significantly modulated by task-induced load. Then, a visible but unexpected stimulus may go unnoticed under high load conditions. By demonstrating for the first time the occurrence of IB in an assembly task context, while simultaneously manipulating both types of load, this research reveals limitations of current HMIs and emphasizes the need to integrate this phenomenon into future interface design through a human-centered approach.

Jülian Salazar, Antonio Capobianco, Flavien Lécuyer, Vivien Schmitt

Conference Proceedings

Ergonomic Impact of Backpacks on Bicycle Couriers

Online supermarkets have experienced rapid growth, especially during the COVID-19 pandemic. According to one forecast, the global online food delivery market will generate annual sales of US $1.38 trillion by 2025. The penetration rate in the meal delivery market is expected to be 28.2% in 2025, resulting in steady growth in the bicycle courier profession. Experiments are to be conducted to determine how the load carried is distributed on the back, how the load affects pressure on the buttocks, and how much of the load is absorbed by the shoulders.The measurements were carried out using an ergometer 8008 TRS 3 from the manufacturer Daum Electronic. The wattage range can be changed in 5-watt increments, which meets the requirements of the tests. The backpacks used for the tests are from the companies Flink and Mjam. Pressure sensors from Tekscan are used to measure the forces acting on the back and buttocks.Pressure and force measurements were used to show the forces acting on a cyclist’s body when carrying additional weight. As expected, a slight difference in the measured force between an upright and a forward-leaning sitting position (as on a racing bike) exists, explaining that a forward-leaning posture means that less force has to be borne by the straps and thus by the back. It was possible to show how the pressure is distributed across the cyclist’s back and buttocks and how great this pressure is. Backpacks from two delivery services were compared. It was found that with a load of 15 kg, the maximum pressure on the back caused by the Mjam backpack was higher overall giving a maximum value of 10 kPa, but was more evenly distributed than the pressure caused by the Flink backpack which gave a maximum value of 9.2 kPa. The Flink model showed that the maximum load carried by the back was 184.2 N, greater than that of Mjam, which was 166.5 N. By testing two different saddles, it was possible to show how pressure is distributed between them. The racing saddle showed pressure peaks up to 59.1 kPa on the sit bones. In contrast, the softer and wider touring saddle showed a more even pressure distribution across the sit bones and perineum, with pressure peaks reaching 50.7 kPa. As expected, measurements of the forces in the shoulder straps of the backpacks showed a decrease with a less upright posture, thus shifting the weight to the back. These results also corresponded with those from the pressure measurement on the back.Several series of measurements should be carried out in the future, with more test subjects to improve the results further. To assess the physiological strain on bicycle couriers more accurately, forces exerted on the handlebars have to be also measured. A relief device explicitly developed for this application has significantly reduced the forces exerted. Nevertheless, the ergonomics of bicycle courier backpacks need to be improved considerably, for example, through more comfortable padding. Comparisons with padding used in mountaineering backpacks showed a significant reduction in maximum forces and pressure exerted on the back of the users.

Thomas Angeli, Georgios Aronis, Sebastian Marth

Conference Proceedings

From Capability to Accessibility: A Usability Heuristics Approach to Space Mission Planning Tools

Designing and operating space missions is a technically demanding and iterative process that requires engineers to balance spacecraft design, mission requirements, and trajectory optimization. Existing aerospace software tools, while technically sophisticated, often lack accessibility and usability, unintentionally excluding users with diverse physical, cognitive, and sensory needs. This gap between capability and inclusivity limits participation in mission design, particularly in educational contexts where accessibility is essential. This paper presents a novel framework for embedding human factors and accessibility principles into the architecture of aerospace mission design tools. Drawing on usability heuristics and Universal Design for Learning (UDL) principles, we develop three core contributions: (1) accessibility heuristics that translate inclusive design standards into actionable requirements for aerospace applications, (2) a comprehensive ruleset for architecture design focusing on progressive disclosure, multimodal representation, and user-centred interaction patterns within mission planning interfaces, 3) improved aesthetics compared to legacy mission planning applications, reducing visual clutter and cognitive load. Together, these artifacts form a methodology for systematically applying usability principles to aerospace system architecture. By embedding accessibility at the architectural level, the platform moves beyond compliance to actively support engagement, comprehension, and collaboration.

Madhusudan Vijayakumar, Duha Ali, Tia Bajaj

Conference Proceedings

Participatory Design of Emergency Response Systems for Highly Automated Vehicles: Insights from Older Adults

Approximately 60 million older adults live in the United States, many of whom face increasing challenges with driving due to age-related declines in sensory, cognitive, and motor functions. As a result, many older adults are forced to modify their driving behavior or stop driving altogether. However, the ability to drive is closely tied to autonomy, independence, and quality of life for this population. Highly Automated Vehicles (HAVs) present a promising solution for maintaining safe mobility for older adults. Yet a persistent concern among potential older users is how HAVs will handle emergency situations. While much of the existing research has focused on preventing road-related incidents, there has been limited exploration into how HAVs should respond once an emergency, whether driving-related or health-related, actually occurs.To address this gap, we designed an emergency response system for HAVs that reflects the specific needs and expectations of older adults. We conducted a series of participatory design (PD) sessions with a diverse stakeholder group, including an older adult, an older adult with cognitive impairment and the care partner, a driver rehabilitation specialist, and members of our human factors research team. Over four remote design sessions conducted via Zoom, we aimed to answer two core questions, one related to our emergency system and the other related to the design of PD sessions. The questions were: (1) What features are essential in an HAV emergency response system to ensure older adults’ safety and comfort in using the technology? and (2) Can online PD sessions effectively support engagement and meaningful contributions from older adults in design?The findings provide promising answers to both. First, the sessions generated several key design recommendations for HAV emergency systems. These included defining a list of emergency events the HAV should respond to, outlining appropriate emergency actions, and establishing a logical sequence for those actions. Participants also emphasized the importance of timely communication with care partners, integration of non-emergency health-supportive features (e.g., hydration, medication, and restroom reminders), and the ability for users to personalize emergency responses through a mobile application with ongoing access to the configuration tool. Additionally, participants provided suggestions for the system’s information architecture and visual design components to enhance usability. Second, participants were able to actively engage in all four remote sessions. Surveys showed strong agreement that Zoom did not limit their ability to contribute, and that the sessions were understandable and meaningful. These results suggest that remote PD can be a viable and effective method for including older adults. Benefits included increased comfort and accessibility, reduced travel burden, and the ability to include diverse perspectives from different geographic areas.Overall, this work contributes to the design of inclusive and responsive HAV systems and demonstrates the potential of remote participatory design as a powerful design tool. Future research should expand this work by including larger and more diverse participant samples, and by evaluating these systems in real-world contexts.

Mahtab Eskandar, Alexandra Kondyli, Kenan Carames, Wayne Giang

Conference Proceedings

User-Centred Design of Industrial Exoskeletons: Addressing Anthropometric Differences to Enhance Performance and Acceptance

Industrial exoskeletons represent a transformative technology with significant potential to revolutionize worker conditions across various sectors by substantially reducing the risk of musculoskeletal injuries and work-related disorders[1]. By providing mechanical assistance and redistributing loads, these wearable robotic systems support the worker's natural movement pattern and reduce the stress on vulnerable body regions such as the spine and joints during physically demanding tasks[2]. This minimizes cumulative fatigue that leads to long-term health complications and enhances accessibility to physically intensive roles that require heavy lifting, repetitive motions, and prolonged static postures, by reducing physical barriers in the workforce[3]. Furthermore, industrial exoskeletons serve as powerful equalizers in the workforce, significantly reducing physical barriers that have historically limited access to specific heavy-duty occupations.This technological advancement has profound implications for workforce diversity and inclusion, particularly in promoting gender balance within traditionally male-dominated industries such as construction, manufacturing, logistics, and heavy machinery operations[4]. Exoskeletons enable a broader range of individuals to perform demanding manual labor safely and effectively, regardless of their natural physical capabilities and endurance[5]. This democratization of physical labor expands employment opportunities for underrepresented groups and addresses critical labor shortages in essential sectors[6].However, realizing this inclusive potential relies on applying human factors in the design and development of the exoskeletons. Industrial workers represent a diverse population with varying anthropometric characteristics, body types, sizes, and physiological differences across genders[7]. To accommodate the wide user range, many exoskeleton developers follow universal sizing strategies, which require developing various sizes of wearables and mechanical components to adapt the exoskeleton for different users. This prolongs development time and complicates manufacturing, thus increasing costs.Alternatively, others pursue a one-size-fits-all approach, designing a device that allows, in rough steps, size adjustment to accommodate all users in the broadest range possible. However, this may fail to adequately address individual users' nuanced biomechanical and ergonomic needs[8]. This compromise between commercial viability and user-specific optimization can result in suboptimal performance, reduced comfort, compromised safety, and ultimately limited adoption rates[9].The consequences of this oversimplified design approach become particularly pronounced when examining user acceptance rates, with evidence suggesting that inadequate customization leads to poor fitting and widespread rejection of exoskeleton technology among end users[10]. This challenge is especially acute when considering female body morphology, where fundamental differences in skeletal structure, muscle distribution, center of gravity, and joint mechanics require specialized design accommodations that generic solutions cannot adequately address[11],[12].The female body typically exhibits distinct anthropometric characteristics, including narrower shoulders, wider pelvic regions, different torso proportions, and varying limb-to-torso ratios compared to males. These factors significantly influence how exoskeletons interface with the body and distribute mechanical loads[13]. When exoskeletons are designed primarily around male anthropometric data or averaged measurements that fail to account for these biological differences, female users often experience poor fit, inadequate support, pressure points, restricted range of motion, and compromised biomechanical assistance[7], [14].To address these critical design shortcomings and advance toward truly inclusive exoskeleton technology, this paper comprehensively applies user-centered design principles to develop an enhanced human-machine interface for industrial exoskeletons. Specifically, we focus on redesigning the wearables (exoskeleton's harness components) that serve as the critical interface between the robotic system and the human body in our StreamEXO industrial exoskeleton platform.Method and Achievements: An active exoskeleton, StreamEXO, with continuously size-adjustable components, was developed to accommodate 90% of the target population, and stability and comfort were assessed during physical working activities. While static fitting was effective across genders, instability was observed in female users during dynamic tasks due to anthropometric differences. The redesign of the wearables with a user-centered approach, based on female-specific body characteristics, significantly improved fit and stability, with an increment of about 15% and reaching a score of 6 on a 7-point Likert scale in device acceptance in a formal test with 15 female subjects.

Christian Di Natali, Pinar Sancandan, Darwin Caldwell

Conference Proceedings

The Application of Dynamic Non-Linear Scales to Aircraft Tape Display Instruments

The presentation introduces the novel Dynamic Non-Linear Display (DNLD) concept. DNLD is a ground-up reworking of the classic tape display format commonly used in aerospace flight instruments and process control systems, based on the application of advanced human factors principles. DNLD reconciles the mutually conflicting demands for adequate precision, sufficient display range, and dynamic legibility, that hamper conventional tape displays. Multiple simulator and flight evaluations have shown how DNLD can confer significant safety, functional, and situational advances in the cockpit, telemetry room, or ground control station. Demonstrated DNLD benefits include complete legibility at very high rates of parameter change, elimination of display saturation, and the ability to keep key parameters and trend vectors in view at all times, without comprising the requirements for fine-tracking performance. DNLD is being adopted by a number of civilian and military flight test organizations. The presentation and paper illustrate the foundational principles of DNLD, including informative videos from DNLD aerospace evaluations. The DNLD concept has been recognized through awards presented by the Society of Flight Test Engineers and the International Test Pilot School (ITPS) at their respective 2023 Annual Symposia.

John Maris

Conference Proceedings

Intelligent renewal method of productive landscape based on the inheritance of Inner Mongolia grassland food culture

Productive landscape refers to a sustainable landscape system formed by combining material output and spatial creation based on agricultural, forestry, animal husbandry, fishing and other production activities. China has a vast territory, significant climate differences between the north and south, and regional integration of food and culture. In Inner Mongolia, a typical representative of northern China, the productive landscape presents unique historical and regional characteristics: on the one hand, the nomadic tradition has shaped the landscape form centered on grassland animal husbandry and dairy product processing; On the other hand, the introduction of farming and gathering activities has enriched the types of dietary landscapes such as grains, fruits, and vegetables. As a carrier of productive landscapes, the inheritance of food culture carries important functions of food supply, national memory, and cultural continuity as a result of the interaction between human long-term food practice and natural environment. Diversified productive landscapes not only support the survival system of regional society, but also have irreplaceable value in ethnic cultural identity and intangible heritage protection. However, current research still relies mainly on qualitative records, with insufficient identification and quantitative analysis of its elements, which hinders the scientific protection and reuse of it. To solve this problem, this article adopts deep learning methods to automatically identify and classify the productive landscapes of typical grassland food culture inheritance background. Based on the ResNet50 model in the PyTorch framework, an image dataset covering landscape types such as pastures, farmland, forest gardens, and fishing grounds is constructed, and preprocessed through size standardization, normalization, and data augmentation. The model is trained with the support of transfer learning and its performance is validated through multiple metrics. The research results indicate that this method can efficiently identify the core elements of food culture in productive landscapes in complex natural environments, significantly improving classification accuracy and stability. Its application value lies in providing a reliable technical path for the digital archiving, dynamic monitoring, and scientific management of food in productive landscapes, aiming to promote the protection, rational utilization, and cultural value transformation of food landscapes, thereby supporting rural revitalization and regional sustainable development.

Xin Tian, Nan Li, Chen Li

Conference Proceedings

Stakeholder Perspectives on Biometrics-Based Multi-Factor Authentication for eIDAS Levels of Assurance: Insights on Usability, Security, and Privacy

Adopting biometrics to an electronic identification (eID) means for online authentication, in addition to its currently popular use for personal device access control, seems a promising solution to achieving both security and convenience of day-to-day online logins. However, the varied ways to implementing biometrics in MFA may raise different concerns of inclusivity, usability, privacy, and regulatory compliance (e.g., the EU’s eIDAS Levels of Assurance Substantial and High). This study explores how stakeholders (users and experts) perceive biometrics-based Multi-Factor Authentication (MFA), focusing on accessibility, privacy, security, and trustworthiness. Eight key questions guided the work, addressing issues such as remote and mobile biometrics, factors’ combination in MFA, biometric data storage, and secret key management, under the context of eIDAS-related standards and guidelines (e.g., BSI TR-03166, ETSI TS 119 461). We surveyed 413 users (Norwegian and English) and interviewed 26 experts across six stakeholder groups: service providers, individual users, academia, eID and biometric technology providers, and authorities / consultants. Results show most users prefer storing biometric data in secure device over cloud services, and oppose shared biometric access (e.g., FaceID) on multi-user devices. Security and privacy were prioritized over convenience by almost two-third of the surveyed participants. Most of them favored MFA combinations adaptive to users’ need. For compliance to LoA High, experts emphasized unique device-user pairing, limited shared access, and the need for multiple factors. They also warned of risks from AI-generated fakes and regulatory uncertainty. Overall, the findings confirmed tensions between usability, inclusivity, and privacy, highlighting the need for flexible, transparent, and accessible biometric MFA designs. Future systems, including the EU Digital Identity Wallet, should ensure privacy-preserving biometrics that meet regulatory assurance levels while remaining usable for all, including elderly and disabled users.

Bian Yang

Conference Proceedings

mHealth Application User Interface Design for Improving Transparency of Healthcare Insurance Spending Progress

Health insurance terminology is often considered confusing by the general public, which leads to making healthcare decisions based on incomplete understandings. This work explores how mobile interfaces can improve insurance literacy and spending transparency. Through a three-phase study with 26 participants enrolled in employer-sponsored insurance, we examined comprehension of fundamental insurance concepts. Our pilot study identified key confusion points, informing the development of a prototype interface iteratively improved through comparison testing. Results reveal that effective mobile interfaces enhance insurance literacy by separating complex concepts into distinct visual components, providing contextual explanations through strategically placed tooltips, and balancing comprehensive information with progressive disclosure. The final design showed substantial improvements in both cost estimation accuracy and user confidence, demonstrating that thoughtfully designed interfaces can transform abstract insurance concepts into comprehensible frameworks that empower informed healthcare decisions.

Ye Tian

Conference Proceedings

Bridging the Gap Between Modern UX Design and Particle Accelerator Control Room Interfaces

Accelerator control systems often represent relatively complex and safety-sensitive human–machine interfaces within process control industries. These systems are technically robust and reflect the cumulative integration of solutions built and adapted across decades. One of the regular, unfortunate casualties of provisional accelerator control system updates is their human-system interfaces (HSIs) which often lag behind modern usability and design standards. An additional challenge is that although there is a multitude of established human factors (HF), and user experience (UX) principles for everyday digital applications, there are very few (if any) established principles for complex and safety-critical applications for an accelerator. This paper argues for the importance of established HF and UX principles (herein referred to as human-centered design principles) into the development of accelerator HSIs, emphasizing the need for clarity, consistency, responsiveness, and cognitive accessibility. Drawing from HF/UX best practices and human-centered design, this paper discusses how these approaches can enhance operator performance, reduce human error, and improve accelerator personnel collaboration. Case studies from Accelerator Control Operations Research Network (ACORN) at Fermilab are explored to demonstrate how interfaces built with human-centered design principles can scale with system complexity while remaining intuitive and efficient for diverse user roles including operators, machine experts, and engineers. By bridging the gap between traditional control system design and modern human-centered design methods, this paper provides a roadmap for evolving accelerator HSIs into more usable, maintainable, and effective tools.

Rachael Hill, Katya Le blanc, Zachary Spielman, Casey Kovesdi, Torrey Mortenson, Madelyn Polzin

Conference Proceedings

A Human-Centered Design Approach: Research on Urban Memory Perception and Preservation Intentions in Urban Villages

Urban villages represent a unique product of China's urban spatial transformation, bearing the social memories and cultural imprints of specific historical periods. During current renewal and redevelopment processes, urban memories often face risks of dissolution, leading to cultural fragmentation and spatial homogenization.Analysis of urban village data in Hohhot reveals that effectively identifying and preserving memory resources within these communities remains an unresolved challenge. This study focuses on the Shi la men geng urban village in Hohhot, Inner Mongolia, China. Guided by a human-centered design philosophy, it constructs an evaluation system for urban memory cognition. Data collection methods include questionnaires and in-depth interviews, with quantitative analysis conducted using a multiple logistic regression model. Centered on the dual dimensions of “material variables—intangible variables,” the study systematically extracted memory elements and characteristics of urban villages. It constructed a multi-level memory perception influence factor system encompassing two primary variables, 23 secondary variables, and 92 tertiary variables. This framework comprehensively reveals the diverse factors affecting memory perception in Hohhot's urban villages and provides an in-depth analysis of preferences regarding the perception and transmission of memory elements. The findings analyzed urban memory elements in village-in-the-city areas and identified their cognitive and transmission preferences. Results indicate that villagers exhibit significantly higher cognitive awareness of intangible memory elements (e.g., village naming, cultural customs) than tangible ones (e.g., building materials, structural forms). Middle-aged and elderly groups demonstrate stronger recognition of cultural customs, while higher-educated groups show greater attention to material carriers.Based on these findings, the study proposes strategies including differentiated transmission of historical and cultural information, and the integration and recreation of material carrier memory elements. These aim to achieve effective preservation and revitalization of urban memory during urban renewal, maintain the continuity and uniqueness of urban memory, and fully leverage the cultural value and social functions of urban villages.

Ziyi Yang, Zhiqiang Fu, Chen Li

Conference Proceedings

The Co-evolution of Museum User Experience and Technology: A Systematic Literature Review

The form of museum user experience (UX) is undergoing a transformation from “information delivery” to “immersive experience” ，in recent years, the rapid development of UX design theory and technology has provided new possibilities for museum experiences. However, museum UX design, technology, and practical applications are still largely situated within relatively independent disciplinary systems ，lack systematic cross-integration ,which makes literature retrieval and comprehensive research difficult. This paper adopts the methods of literature review and case analysis ，analyze the phased development of museums from multiple dimensions: functions, user experience, and technology to form a contrasting framework. Research has found that the development of museum UX lags behind user experience research, technological advancements, and practical applications ，existence a problem of misalignment between theory and practice. The rapidly developing technologies and UX practices have not yet been systematically incorporated into the framework of museology research ，which limits researchers to the knowledge system of a single field. This paper aims to help designers better understand the development process of museums, balance multidisciplinary theories in design, and remind designers to pay attention to the importance of interdisciplinary applications.

Sining Li

Conference Proceedings

Data-grounded empathy: Simulating "the untouchable" to mitigate representational bias in user research

Traditional user research in high-pressure service contexts is often constrained by logistical challenges and the pervasive influence of social desirability bias (SDB), which compromises data authenticity. This paper presents a reproducible workflow for developing and validating a high-fidelity AI interview agent designed to address these challenges. Built on a Retrieval-Augmented Generation (RAG) architecture, the agent is grounded in a multi-source knowledge base compiled from in-depth interviews, online community discussions, and multimedia content from service-industry workers. We describe the end-to-end process, from data collection and preprocessing to agent implementation and prompt engineering. The agent’s performance was assessed through a two-part validation study: an expert heuristic evaluation and a comparative Turing test involving 22 participants. The results show that the agent produced interview data that were perceptually indistinguishable from human-generated responses and were rated by participants as significantly more consistent and coherent. This work contributes a transparent and adaptable methodology for Human–Computer Interaction (HCI) and design research, offering a scalable tool to gather authentic user insights while mitigating known biases. The findings point to a new paradigm for human–AI collaboration in user research, particularly for accessing hard-to-reach populations.

Xiaoman Lin, Yufei Wang, Leran Zhou, Anzhe Huang, Yunmao Gao

Conference Proceedings

Development of a Graphical User Interface for the Advanced Capabilities for Emergency Response Operation’s Portable Airspace Management Concept

Uncrewed Aircraft Systems (UAS) have emerged as a critical tool in modern wildland firefighting operations, providing real-time data collection, mapping, and communication capabilities in areas that may be difficult or dangerous for crewed aircraft to access. Effective integration of UAS into these high-stakes environments requires structured airspace management systems capable of supporting real-time coordination and situational awareness. Building on the foundational concepts of NASA Ames Research Center’s UAS Traffic Management (UTM) system, the following describes the development of a graphical user interface for the Advanced Capabilities for Emergency Response Operations (ACERO) project, focusing on Second Shift Capabilities (SSC), designed for low-visibility conditions. The user interface (UI) integrates data from multiple sources to support airspace management, coordination, and deconfliction. Drawing upon lessons learned from NASA’s Scalable Traffic Management for Emergency Response Operations (STEReO) research activity, the ACERO team developed a robust, field-ready research prototype informed by a structured systems engineering process. Here, we trace the buildup of the UI from high-level systems engineering requirements to its field-ready prototype which was evaluated during a Spring 2025 field demonstration.

Yasmin Arbab, Connie Brasil, Lynne Martin, Gregory Costedoat, Stefan Blandin, Charles Walter, Deborah Bakowski

Conference Proceedings

Experiments conducted in the Egyptian Museum of Turin on public behavior and the potential of participatory activities for inclusive communication

Cultural communication in museums is an extremely prescient issue, especially considering inclusion as central to accessibility. Visitor behavior is a subject in which audience surveys rarely delve and provide curators with precise and practical guidance about how visitors experience the museum. As part of the European META-MUSEUM project, which investigates in detail the public's responses to cultural stimuli, not only from a cognitive but also from an emotional (i.e. neurophysiological) point of view, two experiments were conducted at the Egyptian Museum in Turin, from which useful indications for future solutions can be drawn. The first experiment utilized eye tracker on a sample of volunteers as they visited two Rooms, to monitor their observation patterns, later mapped for analysis. This allows for a visitor centred approach to assess how the curated environment of a museum reaches the public, trends in public engagement with the contents in the room and how the intentions of the curator line up with the experience of the visitor. The second involved a sample of young adult visitors who were invited to take part in co-creation activity: an opportunity for them to interact with the curators, on historic photos of archaeological excavations. While the first experiment shows that very few visitors are able to identify the most important objects, and that their gaze often follows trajectories that are contrary to what the curated exhibition aims for, the second experiment shows that the active involvement of visitors greatly increases their attention and understanding of objects that they had not previously even glanced at. At the same time, the opportunity to contribute to the interpretation of the objects on display greatly increases their self-esteem and therefore their ability to memorize and recall the content: a process open to everyone, regardless of background, ability, or gender. This paper illustrates these experiments in detail and analyses the results, developing some useful considerations for museum professionals to increase the involvement of all visitors.

Michela Benente, Valeria Minucciani, Francesco Paganelli, Daniel John Mangano

Conference Proceedings

Swim shirts optimization for better thermal comfort

Thermoregulation is essential for balancing body heat from metabolism and the environment, especially in sportswear like swim shirts. The aim of the research is to test how well conventional swim shirts provide thermophysiological comfort and how the thermophysiological comfort of swim shirts could be improved by optimizing the material. Following the aim of the research the heat resistance of the materials was measured using the Sweating Guarded Hotplate in laboratory conditions. Then, the surface temperature of the volunteers wearing swim shirts was measured using a FLIR thermal camera, in real conditions, at the indoor pool. A heat distribution model for swim shirts was developed. Experiment analyzed temperature changes of 6 participants, wearing 3 swim shirts of similar raw material composition (PA+EL) but different fabric mass, in 6 body zones on the front side and 6 body zones on the back side. After acclimatization at an ambient temperature of 30°C, participants swam for 10 minutes in 27°C pool water. Imaging showed temperature drops across all body zones, ranging between 1.1°C and 3.2°C. Regardless of the differences in the temperature drop due to the different materials of swim shirts and variations in the body size and age of the participants, all three models of heat distribution in swim shirts show the same characteristics. Heat loss is higher on the back, particularly in the shoulder blade area, then on the back around the waist, with nearly equal average temperature drops in the waist area at the front, followed by the chest area. The smallest temperature drops, both at the front and back, occurred in the abdominal and lower back areas. Therefore, to better maintain optimal thermoregulation, swim shirts should use materials of varying masses to correspond to the zones of greater or lesser heat loss. This can easily be achieved today by using seamless knitting techniques on flat knitting machines, circular knitting machines, or warp knitting machines.

Vesna Marija Potočić Matković, Ivana Salopek Čubrić

Conference Proceedings

Dramatizing Everyday Conversations: A Context-Aware BGM Recommendation System Using Generative AI

Conventional music recommendation systems often rely on predefined emotional values or direct user interaction, making it difficult to incorporate nuanced conversational context. To address this limitation, we propose a novel system that recommends background music (BGM) for everyday conversations based on contextual analysis using a generative AI model, Gemini. Our system transcribes spoken dialogues into text, analyzes the content using Gemini, and then identifies similar scenes and BGMs from a preconstructed dataset composed of 12 BGMs derived from the Japanese TV drama “Ichiban Sukina Hana (My Most Favorite Flower)”. By matching real-life conversations with relatable dramatic contexts, the system aims to enhance the immersion and emotional resonance of ordinary dialogues.We developed a system that takes conversational audio as input and recommends BGMs suited to the conversational context. Using twelve conversation themes, we conducted live conversations and tested whether the expected BGM would be recommended from the audio input. As a result, in 10 out of 12 trials (83.3%), the expected BGM was recommended within the top three ranks. For the trials that fell out of rank, although the conversations were related to the assigned themes, more specific sub-contexts were emphasized (partly diverging from the original intent of the theme), which likely caused other BGMs to be prioritized. Additionally, the actual conversational content did not always match what was anticipated, contributing to recommendations that differed from the target. These findings suggest that refining conversation themes to be more concrete and reproducible would increase the likelihood that BGMs aligned with the themes are recommended appropriately

Maki Sakamoto, Shohta Takahashi, Haruka Matsukura

Conference Proceedings

Effects of a Person’s Facial Expressions in Video on Viewers’ Moods and Facial Expressions: Application to Interface Design

The number of video-sharing sites, such as YouTube, in which it is easy to post and view moving images, continues to increase. This may result in viewers unconsciously experiencing psychological effects from the videos. In this research, we aim to clarify the effects of speakers’ facial expressions in videos on viewers. Our study investigated mood ratings and changes in viewers’ facial expressions while watching different facial expressions of a real human speaker in videos. The results showed ‘Happy Feelings’ and ‘Positivity’ to be rated significantly more highly after viewing moving images in which a speaker smiled while they were talking. The results of this study reveal the potential for making and posting videos in the light of the psychological effects on viewers, and for labelling the videos to enable the viewers to select and watch such videos. It is useful to make and post videos in which smiling persons talk, aiming to make the viewers happy and positive, and for viewers to select and watch such videos when they wish to feel happy and positive. Furthermore, based on the results of our study, we created online learning materials for children to help them enjoy learning English conversation. In the online learning materials, when a user selects the correct answer, the facial expression of the character changes to a smile. This visual feedback is expected not only to inform learners that their answer was correct but also to improve their mood and promote a more positive learning experience.

Kiyomi Yoshioka

Conference Proceedings

Multi-dimensional B2B User Persona: Results from a Systematic Review of Research Methods

In enterprise-level (B2B) services, due to the wide range of requirements, lengthy decision-making processes, and notable variations in usage circumstances, user research is more complicated compared to C2C services. Especially in the field of cloud computing, how to balance online behavioral data, emotional expressions, and business demands has become the core challenge of user research. Traditional single-point surveys are unable to meet this requirement and call for more systematic and multi-dimensional methodological support. This study, through the collection of 153 questionnaires and 7 targeted customer interviews, proposes brand-new multi-dimensional B2B user persona research methods. The core lies in the integration of mixed research methods and dynamic profiling modelling. The former combines qualitative research (deep/telephone interviews) with quantitative research (NPS, online behavior analysis) and introduces real-time session analysis technology to improve the breadth and accuracy of insights; the latter departs from the static tagging model and dynamically generates visual decision heat maps based on behavioral data, graphically presenting the concerns, influence, and information sources of different roles. These methods can compensate for the limitations of current research methods by more correctly capturing the dynamic characteristics of enterprise clients. Through multi-dimensional data integration and dynamic persona methodology, the research findings not only provide scientific support for product design optimization, market and sales strategy formulation but also offer a replicable innovative path for B2B user research.

Xinmiao Shen, Zehui Jin

Conference Proceedings

A heuristic-guided method proposal for early ideation phases: designing for Extended Reality Experiences

Over the past decade, the fast rise and adoption of emerging technologies has shown to be fruitful ground for user-centered experiences. However, despite the technological advancements, the design processes applied to such experiences are marked by challenges, especially in the initial development phases, where they are not widely adopted and whose usage scenarios are not yet consolidated, creating a need for metrics and values for design decisions. This work discloses a design process for creation and early evaluation of features and applications adapted for emerging technologies, as the context of the initiative stood for the use of Agentic AI and wearable devices. The process, entitled Heuristics Evaluation Method for Conceptual Scenarios (HEMCS), ought to be run by a design team and consists of 4 stages: 1) Immersion 2) Diverging 3) Converging and 4) Evaluation. Prior to the application of HEMCS, a series of semi-structured interviews was realized with 12 participants who considered themselves to have productivity issues in their work and/or study environment. Once the main insights from the interview were mapped by the design team, HEMCS could then be applied. The method also required the participation of a multidisciplinary and diverse group of people for the Diverging stage, and a group of UX specialists for the Evaluation stage, applying the Turatti Scale. Both groups composed by people who were not part of the design team, and integrated to the process early, in the Immersion stage, facilitating the sessions. The final report suggested not only potential improvement opportunities, but also more assertive insights about the solutions proposed. The results demonstrate that HEMCS may be used as an efficient evaluator of both technological potential and good user experience in early ideation phases, while adapted for emerging technologies, with significant im-plications for the advancement of user- centered design.

Rodrigo Cavalcanti, Vinicius Mello, Marcos Silbermann, Alan Turatti, Gilberto Oliveira Neto, Bernardo Bulcão

Conference Proceedings

Designing Mudflat Fishing Mobility for Worker Safety and Reduced Physical Strain

Haerujil, a traditional shellfish-gathering practice on Korea’s tidal flats, has recently grown as a form of leisure, yet it poses persistent safety risks due to soft and heterogeneous terrain, nighttime activity, and the aging profile of participants. These conditions frequently lead to disorientation, tidal entrapment, and slips or falls. To address these challenges, this study proposes a human-centered mobility design concept that integrates autonomous driving technologies to enhance both safety and convenience in mudflat environments.Drawing on user analysis and environmental constraints, three key design requirements were identified: reducing physical workload, ensuring stable movement on weak terrain, and preventing isolation accidents. To meet these needs, a suspension-equipped track wheel system was developed to minimize ground pressure and enable stable transport of tools and harvested shellfish. An RTK-GPS–based tracking system provides centimeter-level positioning, while a tide-linked alarm delivers staged visual and auditory alerts for stranding prevention. Ergonomic features such as storage compartments and safety handles reduce strain and support user interaction.The design concept was realized through both full-scale mock-ups and scaled functional prototypes, incorporating autonomous navigation, signaling, and safety interfaces. Findings demonstrate the feasibility of mudflat mobility not only as a transport device but as an integrated work-assistance platform that collaborates with users throughout the harvesting process.This research highlights the importance of combining mobility engineering, autonomous navigation, and human factors design to improve the safety and sustainability of traditional coastal practices. Future work will refine the system through field trials, validation of autonomous functions, and deeper integration with wearable interfaces.

Da Eun Lee, Seung Hyun Seo, Yejin Lee, Kwangtae Jung

Conference Proceedings

Design of a Digital Library Kiosk for public area in Comoros

Access to information and education is crucial for personal growth and community development; however, many regions worldwide face significant barriers in providing these opportunities (World Bank, 2018). The Union of the Comoros is an island nation located at the northern end of the Mozambique Channel in the Indian Ocean. Comoros faces persistent challenges in providing access to educational resources due to financial constraints, prolonged library construction timelines, and centralization of existing facilities in urban areas such as Moroni. Limited access to library resources in schools and public spaces restricts information dissemination and delays educational developments, particularly in rural communities. Due to high costs and logistical barriers, traditional library models remain unattainable for many areas, often leaving incomplete projects in urban centers without benefiting rural communities. This study aims to design a solar-powered digital library kiosk that decentralizes and expands access of educational materials across the country. A user study involving 102 participants from urban, semi-urban, and rural areas, combined with anthropometric and ergonomic analysis, was conducted to identify user needs, literacy levels, and environmental conditions. The resulting design integrates a solar power system for off-grid operation, a multilingual interface to support diverse literacy levels, and multiple content access options, including USB, Bluetooth, email, and printing. Durable, weather-resistant materials and an RFID-based login system enhance users’ usability and security. Findings indicate that the Komo Library kiosk provides a culturally relevant, cost-effective, and scalable solution to improve literacy and educational accessibility in Comoros, offering a sustainable model for bridging the educational gap between urban and rural areas.

Saandi Youssouf, Zhang Xusheng, Amir Ubed, Peter Walusimbi, Daphne Isatou Timbo, Mekontchou Tsane Steve

Conference Proceedings

Analysis of the relationship between interactions by live streamers and viewers and pay-what-you-want donation behavior using LLM

Since the COVID-19 pandemic, people's activities in the virtual world have continued to expand year by year. At the same time, the number of virtual world service recipients who donate money to service providers as a form of support has also continued to grow. For example, the behavior of viewers donating money to live streamers on social live streaming services is expanding year by year on global services such as Twitch and YouTube in the form of social tipping, redeemable digital gifts, and subscription gifting (e.g., TwitchTracker.com, 2025).Since there is no upper limit on the amount of money that service recipients can give to service providers as support, and they can do so repeatedly, this can be considered a Pay-What-You-Want (PWYW) donation. PWYW donation is done through the chat window on social live streaming services. Therefore, PWYW donation is made as a post in the chat window while voice conversations, chats, and emoji interactions are taking place between the live streamer and viewers.With regard to prior research focusing on voice conversations, chats, and emoji interactions between live streamers and viewers, no analysis related to viewers' behavior of “donating money” to live streamers has been found to the best of my knowledge. For example, Reckenwald analyzed conversations and chats between live streamers and viewers to determine what kind of interactions were taking place and what conversational skills were necessary for live streamers, but did not analyze them in relation to the behavior of “donating money” (Reckenwald, 2018).Therefore, by analyzing data from voice conversations, chats, and emoji interactions between viewers and live streamers regarding PWYW donation behavior, and clarifying how these interactions lead to the behavior of “giving money,” it is considered that we can gain many implications, e.g., for marketing plans or new service designs that contribute to promoting PWYW donation behavior among viewers and enhancing the value of services provided by live streamers.In this study, the author proposes a method of analysis using LLM (Large Language Model) to analyze complex interaction data between live streamers and viewers through multiple means, such as voice conversations, chats, and emojis. In recent years, LLM has become capable of summarizing multiple sources of video, audio, and text. This study uses LLM to summarize the voice conversations, chats, and emoji interactions between the two parties, which have not been done before, and attempts to clarify how these interactions lead to PWYW donation behavior, i.e., the behavior of donating money.This study uses Twitch as a case study and focuses on social tipping and subscription gifting as PWYW donation. Using LLM, the study summarizes actual voice conversations, chats, and emoji interactions between live streamers and viewers, analyzes them, and clarifies how these interactions lead to PWYW donation behavior.ReferencesTwitchTraker.com. 2025. “TWITCH SUBS COUNT & STATS”, TwitchTraker.com.URL: https://twitchtracker.com/subscribersReckenwald, Daniel. 2018. “The Discourse of Online Live Streaming on Twitch: Communication between Conversation and Commentary”, Doctoral dissertation, Hong Kong Polytechnic University, Hong Kong Special Administrative Region.

Hisayuki Kunigita

Conference Proceedings

Unveiling Digital Acceptability: A Pathway to Inclusion

This study examined the Technological Environment Usability (TEU) in the context of a pilot project for deploying technologies within a residential setting for individuals with autism, with or without intellectual disabilities (ID). Unlike previous research, this work adopted a comprehensive approach by considering the interplay of factors related to the individual, the technology itself, and the organizational context. To address the lack of standardized tools capable of capturing these multifaceted influences, the research team developed a tailored instrument, the Q-TEU questionnaire, designed to evaluate key factors at three critical phases of the TEU process.Key FindingsThe study highlighted the dynamic and temporal nature of stakeholders’ perceptions during the deployment process, revealing notable shifts across the phases of acceptability, acceptance, and appropriation. These findings emphasize that stakeholders' views are not static but evolve as they engage with the technology, requiring continuous assessment and adjustment to align with their needs and experiences.At Time 0, early evaluations of stakeholder perceptions allowed for the identification of actionable insights, which informed strategies for deployment. These included tailored training materials developed by the research team and implemented by managers to address specific needs. These initial eﬀorts aimed to equip stakeholders with the skills and knowledge necessary for successful technology integration. However, the results indicated only marginal improvements in these areas, likely due to the limited scope of the training, which was oﬀered as a one-time intervention rather than a sustained, iterative process. This limitation underscores the importance of long-term, phased training programs to ensure stakeholders can adapt to and fully benefit from technological changes.At Time 1, following the introduction of the technologies, the Q-TEU scores revealed a dip in stakeholder satisfaction and engagement. This decline is consistent with Gartner’s Hype Cycle, which describes how inflated expectations during the initial stages of adoption can lead to disappointment when the realities of implementation fall short of the anticipated outcomes. In this case, stakeholders likely encountered challenges in integrating the technologies into their daily routines, necessitating additional eﬀorts to recalibrate expectations and adapt the tools to the practical demands of the environment. Despite these initial setbacks, the subsequent assessment at Time 2 demonstrated a rebound, with all Q-TEU scores showing an upward trend, suggesting that stakeholders gradually adapted to the changes as their familiarity and confidence with the technologies grew.One of the notable findings was the recognition of the benefits that the technologies provided to the residents. Stakeholders observed improvements in areas such as task completion, communication, emotional regulation, and autonomy. These outcomes align with prior research on the positive impacts of technology for individuals with autism or ID. However, unlike some studies that suggest technology can reduce the need for human assistance, stakeholders in this project reported that residents continued to require significant support from caregivers. This discrepancy may be attributed to individual diﬀerences among residents, as well as contextual factors such as the frequency and duration of technology use, which can influence outcomes.Limitations and Future DirectionsThis study has several limitations that warrant consideration. First, the absence of standardized instruments to evaluate the interplay of individual, technological, and contextual factors necessitated the development of the Q-TEU. While this tool proved useful, its limitations at Time 0 prevented direct comparisons with subsequent phases, restricting the ability to assess the significance of observed changes over time.ConclusionDespite these challenges, the study highlights the potential of technologies to support individuals with autism or ID in residential settings. However, their successful implementation requires a comprehensive approach that considers the evolving needs and perceptions of all stakeholders. By adopting strategies that integrate sustained training, continuous feedback, and tailored interventions, organizations can foster an environment that promotes meaningful change and maximizes the benefits of technological solutions. This research underscores the importance of multi-level engagement and iterative adaptation in ensuring that technology serves as an eﬀective tool for inclusion and empowerment.

Dany Lussier-desrochers, Karine Ayotte, Rosalie Ruel, Laurence Pépin-beauchesne

Conference Proceedings

Designing transparency for automated driving: Effects of ambient light cues and explanations on driver performance

Autonomous vehicle (AV) demonstrates significant potential to reduce traffic accidents, lower harmful emissions, and enhance mobility, potentially transforming traditional road transportation. However, AV based on artificial intelligence algorithms exhibits characteristics such as black-box nature, uncertainty, and autonomy, posing challenges including low public acceptance and difficulties in human-machine collaboration. In this context, enhancing transparency is crucial for promoting AV adoption, optimizing the human-AV co-driving experience, and improving driving safety. This study employs the Situation awareness-based Agent Transparency (SAT) theory, treating the system’s level of uncertainty in complex driving scenarios as transparency information conveyed via ambient lighting. Driving simulator experiments were conducted, manipulating light pattern (constant, blinking, breathing) and explanatory information provision (presence, absence) as independent variables. Data from 54 participants—including eye tracking, skin conductance, questionnaires, and takeover performance—were collected and analysed.Results indicate that both breathing ambient lighting and explanatory information effectively enhance drivers’ perception of potential risks, with explanatory information significantly reducing takeover reaction time. However, compared to constant lighting, both blinking and breathing patterns substantially increase driver workload, while explanatory information impairs driving stability and reduces driver acceptance and perceived usefulness of AV. This research expands transparency literature, validates the applicability of SAT theory in autonomous driving contexts, quantifies the trade-off between safety and subjective experience, and establishes uncertainty visualization as a distinct transparency construct. It provides a theoretical foundation for improving human subjective driving experiences with AV2. Also, the findings offer actionable guidance for AV transparency design, assisting manufacturers in determining how to design and present transparency information to enhance user safety during automated driving.

Jingyu Huang, Wu Yinglin, Tingru Zhang

Conference Proceedings

Efficiency of Museum Interactive Devices Based on the cidpe Framework

In the global digitalization of culture, museums are confronting a participatory crisis characterized by "physical presence yet cultural absence" among special populations. Traditional service models face a dual dilemma: on one hand, basic accessibility modifications fail to adequately address the needs of visually impaired groups; on the other, high-cost technological upgrades result in only a marginal 7-minute increase in engagement duration for disabled visitors, reflecting a severe imbalance between investment and outcomes. This study, grounded in the CIDPE framework, employs mixed-methods research to investigate the bidirectional experiential enhancement mechanism of interactive museum installations for both disabled and non-disabled audiences. It examines whether accessibility design compromises the experiential resources of general visitors and whether interactive designs tailored for visually/hearing-impaired groups concurrently deepen engagement levels among ordinary visitors. The research aims to reconcile the tension between "cultural inclusivity" and "experience quality" in the digital transformation of museums. A dual-path controlled experiment with a 2×3 factorial design was conducted, involving 345 participants (255 general visitors, 90 disabled visitors) across three museums. Data were collected via questionnaires and heart rate monitoring. Findings reveal that CIDPE-based interactive installations significantly improve cultural cognition completeness for disabled groups and participation depth indices for general visitors, debunking the assumption of mutually exclusive experiences. For instance, a tactile-audio cross-modal interactive system markedly enhances cultural cognition among visually impaired visitors while simultaneously elevating engagement metrics for non-disabled audiences. Multimodal interactive devices achieve demand equilibrium through pressure-sensitive dynamic voice systems and cultural information entropy retention rates, reducing stress response frequency among autistic visitors by 43%. The study constructs a three-dimensional "behavior-cognition-emotion" evaluation model and proposes a modular retrofit solution, cutting costs by 43% while boosting disabled participation rates by 180%. These findings provide both theoretical grounding and quantifiable pathways for transforming public cultural spaces from mere "physical accessibility" to genuine "cultural empathy."

Tuo Zi Xuan, Xin Hu

Conference Proceedings

The Impact of Explanation Design on User Perception in Autonomous Driving Scenarios

The ability of autonomous vehicles (AVs) to communicate their decisions effectively is essential for user trust, safety, and acceptance. Explainable AI (XAI) research in the AV domain has emphasized transparency, yet most studies have focused on what information should be conveyed, when it should be delivered, and through which modality it should be presented. However, limited studies have examined the distinct impacts of rational and affective explanation styles across driving scenarios. To bridge this gap, this study explores how rational and affective explanation styles affect user perceptions in representative autonomous driving scenarios.We conducted an online experiment using a 3 (driving scenario: vehicle following, lane changing, emergency braking) × 3 (explanation style: no explanation, rational explanation, affective explanation) mixed factorial design. Driving scenarios were presented through first-person simulation videos (15–25 seconds), and the explanations in each scenario were provided in both voice and text. A total of 281 participants were randomly assigned to one of the three driving scenarios and exposed to three types of explanations. Following each condition, participants evaluated five dimensions of user perception using validated Likert-scale measures, including explanation satisfaction, perceived risk, trust, emotional experience, and intention to use. After excluding invalid responses, 270 valid samples were analyzed using two-way ANOVA with post-hoc tests. The analysis revealed several key findings. First, explanation style showed a significant main effect on user perception. Both rational and affective explanations significantly reduced perceived risk (F(2, 801) = 12.51, p < .001). Post-hoc comparisons indicated that affective explanations (M_diff = -0.34, p < .001) and rational explanations (M_diff = -0.25, p = .001) were more effective than no explanation. Explanation style also had a significant effect on trust (F(2, 801) = 8.21, p < .001). Participants reported higher trust in both affective (M_diff = 0.27, p < .001) and rational explanations (M_diff = 0.16, p = .04) compared to no explanation. For emotional experience, explanation style demonstrated a significant effect (F(2, 801) = 13.74, p < .001): affective explanations produced more positive experiences than both rational (M_diff = 0.18, p = .032) and no explanations (M_diff = 0.37, p < .001), while rational explanations also outperformed no explanations (M_diff = 0.19, p = .019). Second, driving scenario significantly influenced explanation satisfaction (F(2, 801) = 12.62, p < .001), with emergency braking (M_diff = 0.31, p < .001) and lane changing (M_diff = 0.24, p = .001) resulting in higher satisfaction than vehicle following, suggesting stronger demand for transparency in higher-risk contexts. However, no significant interaction effects were found between scenario and explanation style, indicating stable performance of explanation styles across different scenarios. This study confirms the importance of explanations in critical driving scenarios, extends the scope of XAI research in AVs by highlighting the role of affective explanations, and offers guidance for the design of explanation mechanisms that support transparency, trust, and user experience. More broadly, the findings underscore the value of explanation strategies for human-AI communication in safety-critical domains, contributing to the development of trustworthy and user-oriented intelligent systems.

Shuting Jin, Fang Le, Chen Xingtong, Stephen Jia Wang

Conference Proceedings

User Experience Research on Age-Friendly Products for the Elderly: Case Study in China

In the context of a rapidly aging global population, the needs and well-being of older adults have gained significant attention across social, economic, and design sectors. Within the field of age-friendly product design, Western countries have established a relatively comprehensive and mature system of products tailored to the elderly, while China’s market is still in a developmental and transitional phase with unique socioeconomic characteristics. This study seeks to address this gap by introducing innovative design concepts rooted in cross-cultural comparative analysis. By examining the subtle cultural distinctions that influence product acceptance and usability, this research aims to elucidate the actual needs and underlying preferences of Chinese seniors. The ultimate goal is to propose human-centered design strategies that can enhance the quality of life and overall happiness of the elderly population in China.To achieve these objectives, a multi-method research approach was employed, including structured questionnaires, in-depth user interviews, and systematic collection of feedback regarding the challenges and expectations expressed by elderly users when interacting with both Western and Chinese age-friendly products. The survey sample included 300 participants aged 60 and above from diverse urban and rural regions to ensure broad geographic coverage and balanced socioeconomic representation across different educational backgrounds. Additionally, comprehensive statistical analysis using independent samples T-tests was conducted on the survey data to identify significant differences in preferences, behavioral patterns, and psychological resistance among middle-aged and older adult participants when using comparable products from different cultural origins. The mixed-method research design emphasized both quantitative and qualitative dimensions to capture nuanced and holistic user experiences from multiple perspectives.The results indicate that key factors such as lifestyle conventions, health beliefs, and social support structures significantly shape the product experience of elderly users. These elements help explain the observed high dependence on family assistance, a collective approach to decision-making, and certain unique behavioral habits and emotional needs among Chinese seniors. For instance, products emphasizing individual independence were less favorably received compared to those specifically facilitating family interaction and intergenerational communication. Although existing academic research has increasingly focused on the intersection of aging and technology innovation, there remains a conspicuous lack of in-depth investigation into how cultural dimensions mediate the product experience of elderly users. This is especially true regarding empirical studies that capture the culturally specific preferences and emotional experiences of the elderly within the unique Chinese context.By integrating insights into cultural variability, this study offers tailored product design recommendations that align more closely with the realities and daily lives of Chinese elderly users. It concludes that incorporating culturally adaptive design principles is not only beneficial for product acceptance but also essential for promoting independent living and emotional well-being among the aging population. Furthermore, the research highlights the urgent need for inclusive design policies and educational frameworks that support the implementation and widespread adoption of age-friendly innovations. The findings provide valuable implications for designers, gerontologists, and policymakers aiming to develop sustainable and human-centered aging solutions in China and other similarly positioned societies. Future studies should continue to explore the dynamic interplay between cultural values and technological adoption in aging populations to bridge existing knowledge gaps.

Jiayi Jiang, Xin Hu

Conference Proceedings

Mobile GenAI: Bridging Developer Aspirations and On-Device Realities

The "Generative Renaissance" is rapidly expanding to mobile platforms, promising to redefine user experiences on Android. However, a significant gap exists between developer aspirations for on-device Generative AI (GenAI) and the perceived readiness of the current ecosystem. This paper presents findings from a study of 39 experienced Android developers surveyed at Droidcon NYC 2025. Our results reveal a critical "trust deficit" rooted in concerns over performance, reliability, and security. While developers are actively using GenAI for workflow productivity, they are hesitant to ship user-facing features. We find that developers prioritize robust, secure tooling and transparent performance benchmarks over novel capabilities alone. This study provides a framework for understanding the key barriers to adoption and offers a clear directive for platform and hardware vendors: building developer trust is the essential catalyst for unlocking the true potential of on-device GenAI on Android.

Rojin Vishkaie, Shantu Roy, Laurence Moroney

Conference Proceedings

Evaluating Comfort and Performance with Composite Frequency in SSVEP-BCI

Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) offer high accuracy, fast response, and multiple input options. However, the flickering stimuli used to induce SSVEP can cause discomfort and visual fatigue. Improving user comfort without degrading performance is an important challenge in practical SSVEP-BCI applications. In this study, we developed an SSVEP-BCI using composite visual stimuli that combine high- and low-frequency flickers. Low-frequency flickers typically elicit stronger SSVEP responses, while high-frequency flickers are generally more comfortable. We hypothesized that combining both could improve comfort while maintaining BCI performance. Five stimulus conditions were tested by varying the high-to-low frequency ratio: 0%: 100%, 25%: 75%, 50%: 50%, 75%: 25%, and 100%: 0%. Each participant used the SSVEP-BCI with four inputs under all five conditions. Subjective comfort was evaluated using a 6-point scale. Results showed that BCI accuracy increased with a higher proportion of low-frequency content. The mean classification accuracies for high-frequency ratios of 100%, 75%, 50%, 25%, and 0% were 61.11±1.26%, 95.56±2.79%, 95.28±0.94%, 98.61±2.78%, and 98.61±1.39%, respectively. However, even with a higher proportion of high-frequency content, performance remained at a practically usable level. In contrast, subjective comfort scores increased with a higher proportion of high-frequency content, recorded as 5.67, 4.33, 3.22, 2.00, and 2.44, respectively. These findings indicate that composite flicker stimuli can enhance comfort while preserving SSVEP-BCI performance. Adjusting the frequency ratio allows for flexible optimization depending on the application context.

Sodai Kondo, Hisaya Tanaka

Conference Proceedings

Five-level Drowsiness Estimation Using BlendShape Features Captured by a Smartphone’s Front-facing Camera

This study proposes a real-time, non-contact drowsiness estimation method using a smartphone’s front-facing camera and Apple’s ARKit. While previous studies have primarily relied on eye-blink or facial cues limited to the eye and mouth, our approach captures a wider range of facial expressions, head sway, and eye movement using 52 BlendShape parameters, including features such as eyebrows, cheeks, and nose, along with 3D orientation data. Participants performed a driving task designed to induce drowsiness in a simulator environment, and external raters evaluated their drowsiness on a five-point scale. These ratings were then used as labels to train a K-nearest neighbor (KNN) classifier on features derived from mean values and temporal variances of facial indicators, sampled every five seconds. To enhance model interpretability, SHapley Additive exPlanations (SHAP) were employed to quantify the contribution of each indicator to the classification results. Results show that the amount of movement and standard deviation of indicators—rather than absolute position—were strongly associated with higher classification accuracy. Mouth-related indicators, such as yawning and lip movement, showed particularly high contributions to drowsiness prediction. Using data labeled by external raters on a five-point scale, we performed binary and ternary classification by downsampling from the original five-class dataset. As a result, the proposed method achieved classification accuracy of 98.6%, 89.6%, and 70.5% for binary, ternary, and five-class settings, respectively, with F1 scores of up to 99.3%. These findings suggest that smartphones equipped with ARKit can serve as reliable and accessible tools for detecting drowsiness using facial expression dynamics. Importantly, temporal variation in facial movements—especially head sway and eye closure patterns—proved to be more robust than static features in distinguishing levels of alertness. Future work will optimize feature selection to reduce computational load and improve classification performance, particularly for fine-grained tasks such as five-level drowsiness estimation.

Shunki Suzuki, Hisaya Tanaka

Conference Proceedings

Evaluating Interface Layout, Button Area, and Quantity on Screen Reader Navigation for Visually Impaired Mobile Users

With the widespread adoption of smartphones, visually impaired individuals increasingly rely on built-in screen reader functionalities for daily learning and activities. However, many current mobile interfaces lack inclusive design considerations, particularly in interface layouts and button configurations, leading to decreased operational efficiency and higher error rates. Although prior studies have examined the effects of interface layout, simplification, button area, shape, and quantity on user interaction, most have focused on individual factors. Comprehensive analyses of how these design elements interact are limited. This study collaborated with a Taiwanese school for the visually impaired, recruiting 30 students to participate in an experiment evaluating the combined effects of interface layout (grid vs. list), button area (fixed vs. equally divided), and button quantity (4, 6, 8) on task performance and user preference. Participants completed tasks using 12 different interface configurations, and their performance was assessed based on task completion time, error rate, and subjective preference. Statistical analysis using repeated measures ANOVA revealed that under the four‑button condition, equally‑divided button areas led to better performance (M = 40.2 s, SD = 4.2) compared to fixed button areas (M = 51.5 s, SD = 5.7), F(1, 29) = 8.74, p = .006. The Grid layout produced fewer errors than the List layout (10 vs. 25). Under the six‑button condition, fixed button areas outperformed equally‑divided ones, with mean times of 42.6 s (SD = 3.6) vs. 51.7 s (SD = 3.9), F(1, 29) = 14.38, p = .001; here, the List layout showed fewer errors compared to Grid (22 vs. 33). In the eight‑button condition, though differences were not statistically significant, low‑vision participants preferred the List layout (5 out of 7), while blind participants favored the Grid layout (6 out of 7). These findings offer practical guidance for designing accessible mobile interfaces and support the standardization of assistive technology products.

Hsiang-ping Wu, Chih-fu Wu, Yung-hsiang Tu, Cheung-choi Ching

Conference Proceedings

Parental Behavioral Differences and Psychological Load in Arcade Gameplay: A Case Study on Racing Simulators

Arcade game centers are public leisure spaces where parents frequently accompany their children. Within such interactive environments, parents may act not only as companions but also as facilitators, strategic guides, or behavioral role models. Compared to solo play, intergenerational co-play involves more complex role negotiation and interactional adaptation, placing parents under dual psychological loads from gameplay challenges and social expectations. This study investigates the behavioral patterns and subjective experiences of parents during accompanied play in public arcade settings and identifies practical implications for human factors and interface design. Thirty parent-child dyads were observed interacting with a motorcycle-themed racing arcade game. Using structured naturalistic observation, six behavioral indicators were recorded: decision-making authority, intervention type, gaze behavior, gameplay duration, instructional behavior, and emotional expression. Post-observation interviews were conducted to assess psychological safety, instructional anxiety, and engagement. Results showed that parents during accompanied play most frequently displayed either positive (33.3%) or neutral (56.7%) facial expressions, with only 10% appearing serious. Gaze was predominantly sustained (80%), with intermittent or no gaze accounting for 10% each. In father-child groups, a recurring “parent-directs, child-plays” pattern was observed, indicating a shift toward instructional or performative roles under cognitive and social stress. Chi-square analysis revealed a significant association between gameplay leadership (parent-led, child-led, co-led) and intervention strategy (full physical, mixed, full verbal) (χ²(4, N = 30) = 15.54, p = .004), with a medium-to-large effect size (Cramer’s V = 0.51). When children led, interventions were exclusively full physical (100%), while parent-led play favored full verbal (52.4%) or mixed strategies (33.3%). No full verbal interventions were observed in co-led situations. The study recommends that arcade systems integrate intergenerational interaction needs through shared control interfaces, adaptive task calibration, and anonymous adult modes to reduce pressure, enhance engagement, and support more inclusive public digital play experiences.

Li-hui Yang

Conference Proceedings

Remote Customer Service Interaction Using Avatar Robots: The Influence of Interpersonal Distance and Operator's Visibility

This study aims to determine what constitutes a “good avatar-mediated interaction” during customer service. We experimentally investigated how changes in the surrounding environment during remote customer service via avatar robots affected the interaction. The nature of interactions involving avatar robots is significantly influenced by spatial factors. Specifically, the physical distance between the customer and robot as well as the robot’s camera angle can affect how the customer is perceived by the service provider, which may affect the quality of the interaction. However, these effects are not yet fully understood. The experiment was conducted in a controlled environment involving a participant playing the role of a customer and an avatar robot representing the service provider (who interacted with the customer through the robot). The customer was instructed to face and interact with the avatar robot, while the operator was connected remotely and responded through the robot. We collected speech data (audio and speech-to-text) from the simulated interactions along with eye-tracking data from the operator. After each trial, the participants provided subjective evaluations, including a score for the interaction (0–100; higher is better), stress level rating (0–100; higher is more stressful), and perceived sense of interpersonal distance (five-point scale from “very distant” to “very close”). The findings indicate that greater interpersonal distance between the customer and avatar robot tends to reduce stress. Customers also exhibit higher stress when seated, suggesting that posture influences their emotional responses. When the customer was outside the robot’s camera frame, the operator’s gaze was directed less frequently toward the control interface, implying a disruption in visual engagement. These results suggest that customer visibility and spatial positioning during remote service significantly influence interaction quality.

Manabu Chikai, Kentaro Watanabe, Bach Quang Ho, Jooho Park, Yui Murakami, Kayo Koike, Min Ma, Masahiro Tsutsu

Conference Proceedings

Ready Player AI? Analyzing the Developer Experience for Next-Generation Mobile Gaming

We provide a comprehensive analysis of the global Android gaming landscape, based on a survey of 1,249 senior developers, and reveal a clear and urgent demand for a new generation of mobile gaming centered on the transformative potential of on-device Artificial Intelligence. The findings show this highly experienced developer base is eager to innovate, rating on-device AI/ML features—from performance upscaling to NPU-accelerated game logic—as critically important for their future projects. Their ambition is to deliver higher fidelity and more dynamic experiences that push the boundaries of mobile entertainment. However, the path to this AI-driven future is currently obstructed. While the desire to innovate is strong, the adoption of advanced AI hardware features is primarily blocked by a lack of deep integration within dominant game engines and concerns about inconsistent support across a fragmented device ecosystem. This work explores how emerging technologies that offer hardware-level AI acceleration can provide a direct solution, creating a standardized and powerful baseline for developers. We will examine the advanced, forward-looking use cases this technology unlocks, including AI-accelerated procedural content generation to create near-infinite game worlds, generative texture compression to overcome asset size limitations, and even real-time Neural Radiance Fields (NeRFs) to bring dynamic global illumination to mobile devices. Ultimately, the data presents a clear call to action: bridging the gap between developer ambition for AI and the practicalities of the development platform is the critical next step to unleashing a new era of intelligent, truly next-generation gaming on Android.

Rojin Vishkaie

Conference Proceedings

DeepSeek, ChatGPT, or Gemini? A Multi-Method Investigation of Neural and Behavioral User Experience

As artificial intelligence (AI) tools become increasingly integrated into daily workflows, understanding user interaction patterns with these systems is critical for optimizing interface design and user experience. This study investigates the usability and emotional responses across three prominent conversational AI chatbots: DeepSeek, ChatGPT and Google Gemini, combining traditional usability assessment with neurophysiological measurement using the Emotiv Insight Electroencephalogram (EEG) headset. The research aims to compare AI tools based on user-friendliness and emotional responses, contributing to the development of emotionally adaptive AI.The study included 12 participants ranging in age from 18 to 48, with 75% identifying as female. Prior to the interaction with the AI platforms, the participants completed a presurvey gauging their previous experience and frequency using these platforms. Subsequently, participants completed 5 different randomized task scenarios across all three AI platforms. These tasks consisted of factual Q&A, reasoning and math, code debugging, creative writing, planning and decisions. Simultaneously, EEG data captured real-time emotional markers including interest, excitement, engagement, stress, relaxation, and attention. Tasks were followed by corresponding questions measured on a Likert scale. These questions measured confidence, clarity, helpfulness, creativity, and trustworthiness. After completing all 5 tasks interacting with the tool, the participants completed the User Experience Questionnaire (UEQ) to assess perceived interface quality. UEQ ultimately measures six categories: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty.Preliminary analysis and results suggest that the AI tools differ in terms of their attractiveness, novelty and stimulation. Some participants mentioned they would switch to using a tool that they had tried for the first time during the experimental session.

Keziah Gopalla, Duha Ali, Haneen Ali

Conference Proceedings

A Systematic Review of Ground Truth Labeling and Prediction for Cognitive Workload Adaptive Systems

Cognitive workload monitoring (or real-time inferencing) is crucial for the safe operation of complex human-machine systems, and motivates the development of adaptive automation technologies to dynamically assist operators and prevent both overload and disengagement situations. We systematically reviewed 75 recent studies (2015–2025) on machine learning-based cognitive workload monitoring and adaptive systems. The review focused on three key challenges: (1) ground-truth workload labeling; (2) predictive model generalization across users; and (3) adaptive automation/interface interventions. Approximately 28% of studies were found to rely on retrospective self-report workload scales for ground-truth labels, although some use objective task performance metrics or hybrid labeling approaches. Predictive models were observed to achieve high accuracy for the same individuals they were trained on (subject-dependent validation; mean ~85.6%), but performance dropped when tested on new users (subject-independent validation; mean ~80.3%). In general, the majority of studies present offline model development (for asynchronous classification of workload states) or conceptual system proposals; only 7 studies (9.3%) implemented and evaluated a real-time closed-loop workload-responsive system with human participants. These gaps highlight the need for standardized multimodal workload state labeling methods, cross-user modeling techniques, and empirical validation of closed-loop workload-adaptive systems in operational settings.

Udit Kumar Das, Moajjem Chowdhury, Yunmei Liu, David Kaber

Conference Proceedings

Measurement of User Perception Time for Speed Changes in Virtual Reality

In conventional role-playing and action games, players control characters via a remote or control panel, often greatly enhancing their physical abilities and providing increased entertainment and exhilaration. Recently, head-mounted displays (HMDs) have enabled players’ movements to synchronize directly with in-game characters, offering greater In conventional role-playing and action games, players control characters via a remote or control panel, often greatly enhancing their physical abilities. In conventional games, enhancing a character’s physical abilities leads to increased entertainment and exhilaration. However, in virtual reality (VR) games, the movements of in-game characters and players are synchronized, enhancing the sense of physical presence and immersion. Therefore, the entertainment value of VR games can be enhanced by extending the characters’ physical abilities. This study focused on the perception of speed as a factor contributing to a reduced sense of agency during jump extension. Experiments were conducted to clarify differences in the perception of speed changes between ascent (deceleration) and descent (acceleration), and the results were evaluated using reaction times. The results suggest that during jumping, the sensation of speed may be more easily perceived during the ascent phase than during the descent phase. Therefore, for significantly extended jumps, a trajectory that maintains a constant airborne time while shortening the ascent time and lengthening the descent time may be effective.

Takuto Adachi, Kazunori Kaede, Keiichi Watanuki

Conference Proceedings

Predicting Operator Workload from Oculometric Data in High-Demand Environments: A Case Study with MATB-II

In an increasingly dynamic and fast-paced social context, many professionals are exposed to conditions of cognitive stress that often lead to burnout. Mental workload can be empirically described as the ratio between the available mental resources and the demands imposed by the task. Given this parameter's inherently subjective and non-directly measurable nature, its assessment can be carried out through three main approaches: subjective self-assessment, behavioral analysis, or monitoring of variations in physiological signals. When envisioning the development of a device capable of autonomously estimating an individual's mental workload to support their professional activity, it becomes clear that the self-assessment approach is inapplicable. While behavioral analysis holds interesting potential, it suffers from limitations related to the difficulty of generalizing across heterogeneous scenarios. In contrast, the physiological approach is the most promising, as it allows for monitoring independent of the task. Based on these considerations, the present work aims to develop a system capable of estimating various levels of mental workload using solely ocular signals that can be easily captured through wearable devices such as smart glasses or non-invasive optical devices, like remote cameras.An experimental campaign was conducted with 40 subjects, using the MATB-II (Multi-Attribute Task Battery II) test as the primary task. NASA designed this test to stimulate cognitive workload and multitasking abilities, simultaneously activating various mental processing pathways: visuomotor coordination via tracking, auditory reflex through radio communications, logical reasoning through pump settings, and visual reflex through buttons and control bar management. Due to this multi-level structure, the MATB-II provides complete stimulation of the cognitive spectrum of participants. To explore the full range of mental workload, from minimal to very high levels, a secondary task based on simple arithmetic operations (single-digit addition, subtraction, and multiplication) was integrated into the primary test, with four response options and a maximum execution time. Each experimental session was structured with an initial rest phase to acquire baseline values, followed by five MATB-II trials of increasing difficulty, each interspersed with three minutes of relaxation to restore baseline physiological conditions.From each of the six phases (including the rest phase), a set of features derived from the ocular signal was extracted, which were subsequently used as input for a machine learning training pipeline. The data from each subject were initially corrected against their baseline and then normalized via min-max transformation. The pre-processed data were analyzed to extract the most essential features and then fed into various machine learning classification models. The results show high predictive reliability, outlining promising scenarios for developing automated systems for estimating mental workload. Most tested models can easily separate the baseline and the lower workload state. The distinction between the higher mental workload classes is less evident but still statistically significant.An additional perspective, currently still exploratory, involves analyzing model performance in real-time contexts, using short time windows. This extension would make the system applicable in a wide range of cognitively intensive operational fields, including the automotive, aerospace, and medical sectors, contributing to developing intelligent technologies for continuously monitoring operators' mental states.

Marco Pogliano, Manuel Colavincenzo, Stefano Martorana, Giorgio Guglieri, Danilo Demarchi

Conference Proceedings

Operational Field Study: A Comparison of Piloting Uncrewed Underwater Vehicles and Uncrewed Aircraft Systems

As offshore industries increasingly adopt uncrewed technologies for inspections and operations, the ability to cross-train personnel in both Uncrewed Underwater Vehicles and Small Uncrewed Aircraft System operations has become a focal point for efficiency and workforce optimization. This study presents a comparative analysis of the operational and human factor considerations involved in piloting mini UUV and sUASs, highlighting the key similarities and differences in control methods, environmental influences, navigation, emergency procedures, and situational awareness. A qualitative experimental field study was conducted between July 2024 and October 2024, involving real-world deployments of both systems in maritime and aerial environments. Findings indicated that while UUV and sUAS operators relied on remote control interfaces, sensor integration, and procedural standardization, significant differences exist in environmental, human factors, and control mechanisms. UUV operations required expertise in tether management, underwater currents, and video-based navigation, whereas sUAS operations emphasized GNSS-based positioning, wind resistance, and airspace regulations. Despite these distinctions, aligning control interfaces/mapping and adopting standardized training protocols could enhance operator adaptability between the two systems. The research supported cross-training feasibility in operating both UUSs and sUASs, potentially reducing crew size and operational costs while maintaining safety and efficiency. However, cognitive load management, regulatory compliance, environmental adaptation, and human factors must be addressed to optimize cross-platform competency. Future studies should explore advancements in automation and AI-driven decision support systems and further investigate human factor influences to enhance operational effectiveness in multi-domain applications.

David Thirtyacre, Joseph Cerreta, Peter Miller, Kimberly Luthi, Jolee Thirtyacre

Conference Proceedings

Using Eye-Tracking Metrics to Predict Student Preferences Between a Campus Food Pantry and Alternative Options

Food insecurity among college students causes a significant threat to academic success and overall well-being. According to the National Postsecondary Student Aid Study, more than 4 million students were food insecure during the 2019-2020 school year. While university food pantries work tirelessly to solve this issue, many students remain unaware of these resources or are hesitant to use them. It is important to understand how students perceive and engage with campus food pantries compared to popular campus dining options to improve outreach and reduce food insecurity. While surveys and focus groups can be useful, they may not fully capture the subconscious drivers of decision-making. This study leveraged both survey responses and eye-tracking data to investigate student preferences between a local college food pantry and prominent on-campus food options. Participants viewed 13 paired image scenarios, and Areas of Interest (AOIs) were defined to collect eye-tracking metrics: Time to First Fixation, Total Fixation Duration, and Fixation Count. An Extreme Gradient Boosting (XGBoost) model identified key eye-tracking metrics, using student’s reported food option choices for cross-validation. Results revealed that Fixation Duration was the strongest predictor, suggesting that prolonged visual attention correlated with preference. Additionally, students leaned toward the food pantry for convenience to receive shelf-stable snacks but opted for alternatives when seeking prepared meals. This research supports the development of more effective food assistance strategies that prioritize student needs and behaviors.

Mikaya Hamilton, Chigaemecha Oparanozie, Nicholas Edmond, Steven Jiang

Conference Proceedings

Evaluating Subconscious Response to University Food Pantry Outreach Using EEG & Eye-tracking

Food insecurity, a serious condition where people have uncertain access to nutritious food, severely threatens one’s quality of life. In 2020 alone, 60 million Americans relied on food assistance, with college students disproportionately affected. Approximately 3.9 million undergraduate and 400,000 graduate students experienced food insecurity at a rate more than double of the food insecurity rate among American households in 2020. In response, nearly half of all college pantries have opened in the last five years, yet barriers to access still exist. While improvements in outreach have raised awareness, the psychological impact of language in pantry communications remains unexplored. Traditional methods for evaluating service effectiveness often rely on surveys or focus groups, neglecting unconscious emotional responses and attention that shape students' perceptions of seeking food assistance. This study bridges the gap by employing electroencephalography (EEG) and eye-tracking technologies to measure students’ neural and visual behavioral responses to language framed in neutral, empathetic, and stigmatizing tones. By identifying words that increase engagement or unintentionally reinforce stigma, this research provides evidence-based guidelines for pantries to reduce psychological barriers to pantry use, incorporate language that aligns with student expectations, and reposition outreach material and messages to attract students in need.

Florielvis Hurtado Pernaleta, Mikaya Hamilton, Michael Stubblefield, Steven Jiang

Conference Proceedings

Personalizing Digital Self-Control Tools: Exploring the Role of Decision-Making Styles, Motivation, and Self-Esteem

Personalization is viewed as an important aspect of effective behavioral interventions in designing Digital Self-Control Tools (DSCTs). However, a shared understanding of how to consider users’ individual differences for tailoring self-management interventions is still lacking in the HCI community. To address this, this paper extends previous discussions on three experiential aspects of users’ self-management behaviors with DSCTs: self-esteem, different decision-making styles, and motivation goal types. An exploratory user study was conducted to investigate the effects of users’ decision-making styles and motivation goal types on their levels of self-esteem and self-management behaviors when experiencing self-management contexts. The results of the user study are presented in this paper, which will be used as a theoretical and empirical ground for further design initiatives. Furthermore, the findings highlight the importance of considering users’ individual differences for further design research and practice to create effective DSCTs.

Youngsoo Shin

Conference Proceedings

The Human-Machine Symbiosis Laboratory: A New Testing Environment for Developing Symbiotic Human-Machine Systems

Industry 5.0 envisions a shift in European manufacturing toward human-machine symbiosis, where humans and technical systems operate as adaptive partners. Realizing this vision poses challenges not only for work organization but also for product development of manufacturing machines and technical systems. To validate symbiotic interaction and to systematically generate development knowledge, suitable testing environments are lacking that focus on the interaction between humans and the technical system. To address this gap, the new Human-Machine Symbiosis Laboratory (HMS-Lab) is developed. It integrates three core aspects: simulation of technical systems, measurement of human physiology, and measurement of human perception. A mixed reality setup combines a six-axis industrial robot, a voice-coil shaker, modular handles, and a Unity-based VR environment. For the simulation of the technical system, this equipment enables immersive haptic, visual, and acoustic feedback and allows early evaluation of product behavior without physical prototypes. Human physiology is captured through motion capture for body posture, force plates, and a six-axis force/torque sensor for physical load, EMG sensors for muscle activation, and accelerometers for vibration dynamics. Human perception is assessed with standardized questionnaires and proxy variables such as eye-tracking via VR headsets. By synchronizing physical and virtual components in real time, the HMS-Lab establishes a closed-loop environment for investigating symbiosis. It enables systematic validation of product concepts and provides a scalable basis for building development knowledge, contributing directly to the realization of Industry 5.0.

Simon Saurbier, Sebastian Helmstetter, Susanne Sutschet, Andreas Lindenmann, Sven Matthiesen

Conference Proceedings

Attitudinal changes in self-disclosure through chatbots in career guidance: Factors encouraging disclosure difficulties among students needing support

Previous studies have suggested that chatbots can serve as effective tools to facilitate self-disclosure. This study explored factors that promote the disclosure of negative aspects in career guidance for vocational school students who require reasonable accommodations. The chatbot was designed to provide information that enhances understanding of society, schools, and companies, thereby encouraging free dialogue to deepen self-understanding. Responses were generated by ChatGPT-4o, which referred to pre-prepared external knowledge through retrieval-augmented generation (RAG) to maintain linguistic consistency, simplify complex expressions, and promote positive phrasing. Ten characteristics of chatbots—including immediacy, anonymity, and fairness—were examined in relation to self-disclosure awareness. Participants were screened using ASRS-v1.1 and RAADS-14 for ADHD and ASD tendencies, engaged in dialogues with the chatbot via the LINE messaging platform, and subsequently completed a web-based questionnaire. Although the sample size was small and this study is positioned as preliminary, findings suggest that interactions with chatbots may contribute more effectively than human dialogues to enhancing self- and social understanding among students with ADHD or ASD tendencies. In particular, the characteristics of “anonymity” and “fairness” were found to significantly promote self-disclosure awareness, and providing information related to schools, companies, and society further supported this awareness.

Takayuki Shimizu, Hideaki Kanai

Conference Proceedings

Development of a Wearable EEG Device Toward BCI Applications

Electroencephalogram (EEG) technology is being explored for a wide range of applications, including healthcare, disability assistance, and brain-computer interface (BCI). However, EEG devices commonly used in laboratories are often expensive and not portable. In this study, we developed and evaluated an inexpensive, wearable EEG device as an alternative to the high-performance but immobile EEG1000 system. Long battery life is also a key requirement. Devices with these characteristics are useful for collecting data from individuals who cannot visit laboratories, such as bedridden patients, and for evaluating BCI technology in more practical settings. In particular, for studies involving a large number of participants, low-cost devices can be loaned individually, enabling efficient data collection. The developed EEG device employs a differential amplifier circuit with passive electrodes. It consists of a single-channel analog front-end and a digital section for A/D conversion and wireless transmission to a PC via Bluetooth Low Energy (BLE). The passband is 0.159–100 Hz, the sampling rate is 1 kHz, and the resolution is 16-bit within a 0–3.3 V range. Transient analysis, AC analysis, common-mode rejection ratio (CMRR), and noise analysis were conducted using a simulator. Additionally, alpha waves were recorded under eyes-open and eyes-closed conditions. These measurements were conducted simultaneously with the EEG1000 for comparison, serving as a fundamental test for BCI applications. In all 10 participants, the expected increase and decrease in alpha activity were observed. However, the alpha response was more clearly detected with the EEG1000. Future improvements will focus on enhancing performance through the adoption of active electrodes and multi-channel configurations.

Hisaya Tanaka, Sodai Kondo

Conference Proceedings

Beyond Full Flight Simulators: Investigating Mini Motion Platforms in Helicopter VR Simulation

Virtual Reality (VR) is increasingly being explored as a cost-effective and flexible alternative to traditional full flight simulators for flight crew training. In addition to reducing costs, VR-based simulators offer greater versatility by integrating a range of Commercial Off-The-Shelf (COTS) components. One such component is the mini motion platform—compact motion devices that are significantly smaller and less complex than the full-scale hexapod platforms typically used in full flight simulators. However, their limited motion range raises questions about the extent to which they can meaningfully contribute to the realism and effectiveness of flight training.This study investigates the integration of a mini motion platform, paired with a classical washout algorithm, in a VR-based helicopter flight simulation environment. Using a setup that includes COTS helicopter controls, a mini motion platform, and a Varjo XR-3 headset, participants performed a series of helicopter flight tasks. Data was collected across scenarios with and without motion support, focusing on metrics such as user comfort, pilot performance, and subjective user experience.The results shed light on both the opportunities and limitations of using mini motion platforms in this context. In particular, they underscore challenges in cueing highly dynamic helicopter maneuvers, while also identifying specific areas where such platforms can enhance training outcomes. The insights from this research contribute practical recommendations for leveraging mini motion platforms to support effective and immersive VR helicopter flight training.

Guido Tillema, Boris Englebert

Conference Proceedings

Exploring Augmented Reality Applications in Botanical Gardens: A Pilot Study on Overcoming Seasonal Barriers

Botanical gardens face persistent challenges with seasonal limits hindering plant observation and visitor engagement. This study examines how augmented reality (AR) addresses these barriers by enabling interaction with 3D models of off-season plants, offering visitors experiences otherwise unavailable during certain times of year. At Tsukuba Botanical Garden, field experiments let visitors manipulate digital models created by photogrammetry, providing an immersive, interactive layer beyond physical exhibits. Stamp rallies with quiz elements boosted participation. This paper focuses on the AR experience: forty participants of various ages moved freely through greenhouses and used AR to access details of six plant species, regardless of seasonal display. Building on these interactions, the survey showed that 95% of participants felt satisfied with the AR experience, and even those with little prior interest in plants engaged highly. Further analysis found a weak positive but non-significant correlation between participants’ prior interest in plants and their satisfaction with the AR observation. Participants also reported that AR deepened their understanding of plant structures and let them observe flowers not in bloom at the time. Taken together, these findings suggest that AR can significantly boost educational value and visitor motivation, highlighting its potential as a tool for inclusive, year-round botanical education.

Miki Namatame, Chie Tsutsumi

Conference Proceedings

Interactive Visualization for Human-in-the-Loop 3D-to-2D Pose Annotation

Aligning 3D objects with their poses in 2D images has traditionally relied on manual trial-and-error rendering, where annotators repeatedly adjust parameters until the object appears to match the scene. This process is not only slow and labor-intensive, but also cognitively demanding, leading to human fatigue and inconsistent results. The reliance on such tedious workflows makes it difficult to scale annotations across entire video sequences, while the increased likelihood of error limits the reliability of the generated data.To address this gap, we present an interactive 3D-to-2D visualization and annotation tool that aids in accurate human annotation of 3D object poses. To our knowledge, this is the first system that allows users to directly manipulate 3D objects within a 2D real-world scene, providing an intuitive 3D graphical user interface for annotating object positions and orientations. The tool integrates visual cues with spatial context to enable robust 6D pose annotation. By offering real-time visualization, depth estimation, and both single- and multi-object linked pose annotation, the proposed tool establishes a practical foundation for generating accurate pose data. By reducing the burden of manual trial-and-error and making pose annotation more intuitive, this tool advances human involvement in dataset generation, enabling researchers to more efficiently and accurately create the data needed to drive progress in AI and vision-based applications.The highlights of our proposed augmented reality 6D pose annotation interactive tool are summarized below:1. Immediate and Intuitive Feedback: The interactive visualization provides immediate, continuous feedback, reducing cognitive load and supporting users in forming a clear mental model of the 3D-2D alignment.2. Cognitive Support for 3D Reasoning: By making depth cues explicit, the system supports human perceptual limitations in interpreting 3D structure from 2D views, minimizing errors caused by ambiguity.3. Precision with Reduced Frustration: The single-object annotation mode enables focused, high-precision interaction, reducing task complexity and minimizing accidental misalignment.4. Linking Poses with Context Preservation: By linking multi-object poses in the annotation tool, the system maintains spatial consistency, helping users preserve context and avoid repetitive manual corrections. This reduces annotation fatigue and supports efficient workflows in complex scenes.This interactive tool is open-source and publicly available at https://github.com/InteractiveGL/vision6D.

Yike Zhang, Eduardo Davalos

Conference Proceedings

Cognitive and Performance Effects of Latency and Sensitivity in Drone Control: A Neuroergonomic Perspective Across Skill Levels

As small Unmanned Aerial Systems (sUAS) become essential in emergency response, defense, and public safety, understanding how interface parameters shape cognitive workload is vital. This study examines how latency (low, medium, high), joystick sensitivity (low, medium, high), and pilot expertise (novice, intermediate, advanced) interact to affect performance during complex navigation and object-detection tasks. Using a mixed-methods design, participants operated in controlled simulations while electroencephalography (EEG) measured theta and alpha activity, markers of mental effort and attention. Results reveal that high latency and extreme sensitivity elevate cognitive strain, particularly in novices, while experienced pilots display adaptive resilience yet suffer under mismatched configurations. Elevated frontal theta indicates compensatory effort during delayed feedback, and alpha suppression under high sensitivity reflects focused attention. Optimal workload balance emerges under low-latency, medium-sensitivity settings. Findings inform EEG-driven adaptive interfaces that dynamically tune control parameters, enabling cognitively optimized, skill-aligned, and sustainable drone operations across high-stakes missions.

Suvipra Singh

Conference Proceedings

Assessing Spatial Relations under Altered Frames of Reference: A Virtual Reality Study Using the Mental Cutting Test

Human performance in technical and operational environments depends greatly on spatial ability, the skill to imagine, interpret, and mentally manipulate relationships between objects in space. This ability supports essential tasks in design, engineering, and construction, where professionals must visualize complex forms and predict how parts fit together. In altered environments such as microgravity, the natural alignment between the body’s sense of upright (the idiotropic axis) and the visual frame of reference can be disrupted, which may weaken spatial reasoning when people must mentally cut or rotate objects without stable cues.This study tested how misalignment between visual and bodily reference frames affects spatial relations using the Mental Cutting Test (MCT) in immersive virtual reality (VR). A total of 233 participants completed the MCT under three conditions: (1) Control (CC), with aligned axes; (2) Static Misalignment (EC1), with a fixed tilt; and (3) Dynamic Misalignment (EC2), with continuously shifting orientation. These VR scenarios simulated settings with reduced gravitational cues to probe spatial reasoning in microgravity-like contexts.Results showed a clear drop in accuracy under dynamic misalignment (EC2) compared with CC and EC1, while EC1 did not differ from CC. Response times were comparable across conditions, indicating that the performance loss in EC2 reflected accuracy rather than speed. Demographic analyses showed moderation by gender and gaming experience: participants with regular gaming experience, and male participants, performed better under EC2; age showed no significant effects. From a human-factors perspective, these findings point to the need for training that prepares users to maintain spatial precision when visual and bodily frames are misaligned. VR provides a practical platform for assessing these risks and for designing targeted interventions for space, underwater, and other disorienting operational settings

Faezeh Salehi, Manish Dixit

Conference Proceedings

The use of eye-tracking in maritime simulator-based training

Incorrect human behavior is a significant contributor to maritime accidents. Navigation skills therefore represent a critical factor for safety at sea. Integration, digitalization, and intelligent navigation technologies impact Maritime Education and Training (MET).The study aims at understanding how maritime experts and maritime trainees allocate their visual attention to avoid collision during intense maritime traffic in a full-mission bridge simulator. A sample of two experienced active navigators and seven maritime students were fitted with a wearable eye-tracker and placed in different navigational watchkeeping simulation contexts. Individual visual attention was quantified through the analysis of areas of interest (AOIs) and gaze shifts between these AOIs.Experts and novices differ in their gaze patterns. The most prominent difference is that experts make less use of instruments and look out more for information gathering. As ships are complex socio-technical systems, the results of this study may provide Integrated Bridge Systems (IBS) designers and MET professionals with useful insights on the interaction between humans and navigation instruments.

Anne Bouyssou Chen, Magnus Nylin, Franklin Nyairo, Emilia Lindroos

Conference Proceedings

A Conceptual Framework for AI-based Explainable Driver Behavior in Human-in-the-loop Simulators

This paper shows a conceptual framework for emulating human driving behavior in driving simulators in a generic and adaptable way. It focuses on concepts a) to explicitly include mixed-traffic scenarios on one side and b) to generalize the driver behavior within the simulation in a way to be able to obtain AI-based behavior clusters which are interpretable to human characteristics and human state expressions. The underlying use-cases, its advantages and the underlying setup are intended to feed autonomous driving algorithms with different kinds of pedestrian behaviors like drunk or tired drivers or cyclists. The necessity of this paper is clear: in recent years, the development of autonomous driving has been accompanied by a series of optimistic assumptions. However, despite significant progress, the road to fully autonomous vehicles capable of seamlessly handling all possible driving situations remains an ongoing challenge. As autonomous technology continues to advance, new and complex challenges are emerging. One of the most prominent challenges is navigating in mixed traffic scenarios, in which the road is shared by different entities, including automated and autonomous vehicles, traditional manually driven cars, as well as vulnerable road users, such as cyclists and pedestrians. Understanding, predicting, and replicating human driving behavior in these complex and dynamic environments has emerged as a central but challenging fact of autonomous driving research. The need to address this challenge is not only rooted in safety concerns, but extends to the broader goals of gaining public acceptance and trust in Artificial Intelligence (AI), particularly in the area of self-driving cars. Even assuming that autonomous driving technology is fully mature today, mixed traffic scenarios are expected to persist for several decades. Today, research efforts which aims to model mixed traffic differs in its approaches. Mathematical, mesoscopic and macroscopic approaches exist on one hand for complete traffic flows and usually possess a high level of abstraction of the simulation environment like weather conditions, texture etc. Other conventional approaches use so-called Human-in-the-Loop (HITL) simulations to study driver behavior under different, but “closed” conditions. For example, Kraus developed a behavioral model for lane-changing maneuvers that focused on different psychological aspects of drivers, including fear and happiness, but it did not consider mixed traffic and it focused on the closed scenario of lane changing, so the purpose of the model is not to generalize driving situations. In our approach to generate generic and adaptable mixed traffic scenarios, clustering techniques are first used to categorize drivers with similar behaviors based on variants like k-means, hierarchical clustering, Density Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Models (GMMs). These techniques use features such as acceleration, braking, lane-changing behavior, and reaction times to form clusters that represent different driving styles, such as aggressive, cautious, or normal driving and different human states like fatigue. Second, the Explainability of AI-based clustering is not always given but necessary in the automotive industry to specifically train and test autonomous cars with dedicated, usually critical driving situations. Consequently, the mapping of AI clusters to driving types will play a further role in this paper and in our overall conceptual framework for emulating human driving behavior.

Patrick Rebling, Reiner Kriesten, Philipp Nenninger

Conference Proceedings

Exploring Kinetic Meditation as an Emerging Frontier in Technology-Assisted Mindfulness: A Comparative Review

Mindfulness has gained increasing attention in digital health research due to its demonstrated mental and physiological benefits (Plencler et al., 2024; Yusim & Grigaitis, 2020). While technologies such as mobile apps, wearables, and virtual reality (VR) have increasingly supported mindfulness practices, the majority of interventions continue to emphasize static meditation such as seated breathing and body scanning (Chandrasiri et al., 2020; Zafar et al., 2020). In contrast, kinetic meditation, including yoga, Taiji, and mindful walking, engages both body and mind through slow, intentional movement and embodied awareness, offering a more holistic approach to well-being (Barton et al., 2024; Niksirat et al., 2019). Despite this potential, the incorporation of kinetic meditation into technology-assisted mindfulness remains limited and underexplored in the current literature. To investigate this gap, we propose the following research questions aimed at identifying the overlooked role of kinetic meditation in technology-assisted mindfulness and exploring future opportunities for its integration into digital design.RQ1: What underexplored areas exist in current technology-assisted mindfulness research?RQ2: How can future mindfulness technologies be designed to support kinetic meditation practices?This study first analyzes a dataset of 3,053 peer-reviewed publications to examine publication trends and disciplinary contributions in technology-assisted mindfulness research. It then conducts a comparative review of 10 empirical articles from top-tier HCI journals and 30 peer-reviewed empirical studies retrieved from the Web of Science database (2014–2024). Using qualitative thematic coding, we examined technological applications, mindfulness categories, and sensory design strategies to identify key characteristics of the field, reveal differences between high-impact and broader empirical studies, and highlight underutilized design opportunities in the leading HCI literature.Across both sources, we find that technology-assisted kinetic meditation remains underrepresented, with the majority of interventions continuing to emphasize static meditation delivered through VR-based visuals, ambient soundscapes, and audio guidance (Lee et al., 2023; Payne et al., 2024). Despite its limited presence, the kinetic interventions identified in both top-tier and broader studies consistently demonstrate notable advantages: enhanced presence enabled by visual-auditory immersion, deeper interoception supported by movement-synchronized vibroacoustic feedback, and greater user engagement through motion tracking (Barton et al., 2024; Le Roy et al., 2024; Niksirat et al., 2019). These features suggest that kinetic meditation may provide more dynamic and integrative support for psychological and physiological regulation than static formats. However, most existing implementations remain constrained by passive content delivery, a narrow focus on individual stress relief, and an overreliance on visual and auditory design, with limited incorporation of haptic or other multisensory interaction elements. This review highlights future design opportunities to develop more active, adaptive, multi-sensory, and socially connected environments that foster embodied awareness, sustained engagement, and holistic well-being through technology-assisted kinetic meditation.In conclusion, this review outlines a theoretical direction for immersive environment design within the digital health domain, where embodied interaction, physiologically responsive feedback, and multisensory immersive media support both psychological and physiological regulation. Practically, it offers insights for developing next-generation mindfulness technologies that move beyond static practice toward more adaptive, immersive, and socially connected kinetic meditation experiences for diverse users.

Mengru Liu, Anthony Kong, Fuxuebing Huang

Conference Proceedings

Exploring factors influencing recovery process of visual fatigue and virtual reality sickness

This study examined factors influencing the recovery process of visual fatigue and virtual reality (VR) sickness. Two experiments investigated how factors affect symptom reduction after VR exposure. In Experiment 1, body movement (standing vs. walking) and visual motion (roll vs. pitch inclination) were manipulated. While no significant effects appeared in SSQ scores, visual motion influenced focus-related symptoms in the VISQ, suggesting different recovery dynamics for visual fatigue. Experiment 2 tested control methods (arm swing, controller, auto) and found that symptoms decreased over time regardless of condition. Overall, recovery from VR sickness was time-dependent, whereas visual fatigue was more sensitive to visual motion. These results highlight distinct recovery mechanisms and emphasize the importance of adequate rest after VR exposure.

Tzu-yang Wang, Hiroyasu Ujike

Conference Proceedings

Cognitive Workload and Interface Performance: A Neuroergonomic Comparison of VR, AR, and Traditional Drone Control Systems

As small Unmanned Aerial Systems (sUAS) become vital tools in sectors such as disaster response, inspection, and precision operations, understanding how interface modality shapes pilot cognition is critical. This study compares Virtual Reality (VR), Augmented Reality (AR), and Traditional (physical controller) interfaces under simulated conditions to isolate neurocognitive differences among novice, intermediate, and expert drone pilots. Real-time electroencephalography (EEG) recorded theta, alpha, and beta wave activity as participants completed standardized flight tasks including spatial navigation, obstacle avoidance, altitude stabilization, and precision landing. EEG metrics captured continuous variations in cognitive workload, attentional engagement, and sensorimotor regulation across skill levels. Results indicate that VR induced elevated beta activity linked to sensory integration demands, AR maintained balanced alpha–theta dynamics reflecting optimal engagement, and Traditional control minimized workload through procedural fluency. These findings contribute neuroergonomic insights for developing skill-adaptive, cognitively optimized sUAS interfaces that enhance performance, learning, and operator well-being.

Suvipra Singh

Conference Proceedings

Creating a Lightweight Unity Interaction Package.

This project proposes a lightweight interaction system for VR in the Unity game engine. The Unity VR start up project is ~2GB in size upon creation, while our proposed system is currently ~350MB. It also shrink the needed components by half, while still support most of the same functionality of Unity XR Toolkit. The new system is designed with the goals of supporting non-coders while allowing extensions for coders and following well established GUI event paradigms for familiarity. The project currently focuses on grab base interactions and navigation.

Lisa Rebenitsch, Muhammad Shaharyar, Diego Akantuge, Minati Alphonso

Conference Proceedings

Toward Human-Centered Swarm Control: A VR-based UAV Simulator for Training and Cognitive Evaluation

This paper introduces a virtual reality (VR)-based unmanned aerial vehicle (UAV) simulator designed to support immersive training and early-stage human factors evaluations in rescue and emergency mission contexts. The system simulates a leader-follower UAV configuration, where a human operator controls a lead drone through a VR headset and joystick, while autonomous drones maintain any geometric formations using onboard sensing and dynamic obstacle avoidance [1]. This swarm-based coordination reflects real-world search and rescue scenarios, where rapid decision-making, spatial awareness, and teamwork are essential.The simulator is developed with a focus on human-in-the-loop design, providing an immersive teleoperation experience that places the user in high-pressure environments with dynamic spatial constraints. Visual and auditory feedback, along with the ability to switch between multiple drone perspectives, is intended to support situational awareness, mission control, and error recovery in complex terrain.From a human factors perspective, the simulator serves as a flexible testbed for evaluating cognitive and ergonomic variables in safety-critical tasks. It allows the assessment of operator workload, interface usability, attention allocation, and situation awareness under realistic but controlled conditions. Subjective evaluation tools such as the NASA Task Load Index (NASA-TLX) and the Situation Awareness Rating Technique (SART) [2] can be embedded directly within the VR experience. At the same time, objective data from head and hand movement, control inputs, and UAV performance are recorded to gain insight into operator behavior.The simulator integrates high-fidelity UAV dynamics using closed-loop reference model adaptive controllers [3], and can be equipped with tools such as eye tracking, physiological sensors for workload estimation, and visual attention assessment methods like ATTENDO [4]. These additions support deeper analysis of how operators acquire information, prioritize tasks, and shift attention during mission-critical events.Overall, the VR-based UAV simulator addresses a growing need to evaluate and enhance human performance in multi-agent control systems used in emergency operations. It offers a scalable, immersive environment that supports both training and cognitive engineering. Its modular structure allows for future integration of real UAVs, digital mapping capabilities, and adaptive interfaces, making it a valuable platform for advancing human-centered UAV system design.References [1] Saunders, J., Call, B., Curtis, A., Beard, R., & McLain, T. (2005). Static and dynamic obstacle avoidance in miniature air vehicles. In Infotech@ Aerospace (p. 6950).[2] Braarud, P. Ø. (2021). Investigating the validity of subjective workload rating (NASA TLX) and subjective situation awareness rating (SART) for cognitively complex human–machine work. International Journal of Industrial Ergonomics, 86, 103233.[3] Eraslan, E., & Yildiz, Y. (2021, December). Modeling and adaptive control of flexible quadrotor uavs. In 2021 60th IEEE Conference on Decision and Control (CDC) (pp. 1783-1788). IEEE.[4] Oberhauser, M., & Dreyer, D. (2017). A virtual reality flight simulator for human factors engineering. Cognition, Technology & Work, 19, 263-277.

Emre Eraslan, Avinash Gupta

Conference Proceedings

SMART VR for Commercial motor vehicles Safety: A Scalable Virtual Reality Framework with AI-Driven Hazard Simulation and Physiological Monitoring

Commercial motor vehicles (CMVs) are vital to national logistics but remain disproportionately involved in high-severity crashes, with human factors such as fatigue, distraction, delayed hazard recognition, and cognitive overload contributing significantly to crash risk. Despite advancements in regulation and vehicle technologies, conventional training methods still fall short in preparing CMV drivers for unpredictable, high-risk environments. These approaches often rely on passive instruction or low-fidelity simulation, offering limited realism, adaptability, and behavioral insight. As a result, they struggle to address evolving hazards, monitor physiological states such as fatigue or attentional lapses, support effective skill transfer, or replicate critical scenarios for evaluation and intervention. To address these gaps, we present SMART VR, a scalable and modular virtual reality framework for CMV safety training and human factors research. Built on the CARLA simulator and Unreal Engine, SMART VR provides a unified, high-fidelity platform that integrates immersive simulation, AI-driven hazard generation, and physiological monitoring, supporting deployment through VR headsets and full-scale cockpit hardware with force-feedback steering and operational controls. A configurable scenario engine dynamically injects hazards, from lane incursions, visibility loss, erratic traffic behavior, and auditory distractions, based on predefined or adaptive logic, with each event precisely time-aligned with vehicle telemetry (speed, braking, steering, lane position) and real-time physiological monitoring via wearable sensors capturing eye gaze, heart rate variability, and electrodermal activity. These synchronized data streams enable multidimensional assessments of driver state, including attentional focus, cognitive workload, and stress response, addressing a critical gap in conventional training systems. The framework’s modular design enables the import of custom road environments, integration with external tools such as decision-support systems, and development of targeted training protocols. This flexibility supports the replication of high-risk operational scenarios under controlled conditions and enables repeatable, simulations for validating safety interventions, driver-assist technologies, and human–machine interface designs, advancing CMV training, behavioral evaluation, and intelligent transportation systems.

Kelvin Kwakye, Judith Mwakalonge, Barbara Adams, Stanley Ihekweazu, Nana Gyimah

Conference Proceedings

Demonstrating the Need for Application-Level Design Guidelines in In-Vehicle Augmented Reality to Alleviate Motion Sickness: A Field Study

With fully autonomous vehicles on the horizon, promising new opportunities emerge for productivity during travel. However, motion sickness remains a significant barrier. This study investigates whether video-passthrough (VPT) augmented reality (AR) can reduce motion sickness when working in a moving vehicle. Specifically, we compare the Apple Vision Pro (AVP) Head-Mounted Display (HMD) with a traditional tablet device to assess and compare their impact on motion sickness. The investigation is split into two parts: (1) a main field-study with 40 participants performing visual tasks with both AVP and tablet while traveling in a vehicle and (2) a control-study to evaluate the impact of the device's technical specifications on motion sickness response. Our results indicate that motion sickness occurred less frequently with the AVP compared to the tablet, though the difference was not statistically significant. Severe nausea was exclusively reported during AVP use, though only by a small number of highly susceptible participants who had previously experienced symptoms with the tablet. Our findings also suggest that technological factors such as display resolution, image clarity and Photon-to-Photon (P2P) latency of the AVP at most lead to minor discomfort or mild nausea in highly susceptible individuals and do not trigger moderate or severe motion sickness. The results discussed in this work emphasize the need for design guidelines and standards to ensure in-vehicle AR applications are accessible without inducing motion sickness.

Zack Walker, Ansgar Gerlicher

Conference Proceedings

Combined Effects of VR and Gaming Expertise on Precision Performance

The rapid integration of VR in various application domains necessitates a deeper understanding of how levels of user experience impact user performance and task efficiency. We investigate the relationship between experience with VR, 3d computer games, and physical skills across multiple performance metrics. In a comprehensive analysis of multiple levels of VR experience and 3d computer-game expertise, we identified key trends indicating that increased experience in both domains significantly enhances task efficiency while reducing perceived workload and improving task accuracy. Notably, more experience in VR and expertise in controlling 3d computer games consistently correlate with lower task-load scores and more stable performance metrics. Interestingly, we found that physical space requirements remain consistently low across all experience levels, highlighting the accessibility of existing VR technology.

Mohammad Jahed Murad Sunny, Jan Springer, Aryabrata Basu

Conference Proceedings

Exploring Interpersonal Distance with Virtual Agents on a Naked-Eye Stereoscopic Display

This study investigates how virtual agents displayed on a naked-eye stereoscopic (3D) screen influence interpersonal distance, as well as discomfort and likeability, during a common corridor passing task. The increasing adoption of "on-screen agents"—virtual body agents displayed on devices such as smartphones and large public screens—offers an alternative way to physical body robots, reducing their high costs and potential physical risks. However, a question remains: can these virtual agents enable users to maintain appropriate interpersonal distances in real-world scenarios? In particular, traditional 2D displays often fall short in conveying the sense of physical presence that humans instinctively need to maintain a comfortable personal space. This deficiency highlights a major challenge in designing natural and effective human-agent interactions.This study directly addresses this gap by focusing on the maintenance of interpersonal distance in dynamic, pre-interaction scenarios within a simulated environment, specifically tailored for immersive technologies. We set out to answer two key research questions:•RQ1: How does interacting with an on-screen agent presented on a stereoscopic display affect the maintenance of interpersonal distance?•RQ2: How do the display conditions (3D vs. 2D) of the on-screen agent affect user comfort and likeability during dynamic interactions?To investigate these questions, we leveraged a Looking Glass naked-eye stereoscopic display, that offers multiple viewpoints without any wearable devices, providing a robust platform for simulating depth perception. We designed a controlled experiment where participants walked past a human-like 3D agent in a simulated corridor. The experiment employed a within-participants design across three different conditions: OFF condition (the display turned off), 2D condition (stereoscopic display function disabled), and 3D condition (stereoscopic display fully enabled). To consider potential order effects, all 15 Japanese participants (aged 21-28 years) experienced all three conditions in different orders. During the task, we precisely measured the shortest distance that participants reached while passing in front of the agent. After completing the task, participants rated their discomfort using the Japanese translation of the RoSAS scale and their liking for the agent using the Godspeed scale. All statistical analyses were conducted using Wilcoxon signed-rank nonparametric tests with Bonferroni-Holm corrections.Findings regarding interpersonal distance in RQ1 showed the following key findings:•There was a significant difference between 3D and 2D conditions (3D condition: Mean = 1046.67 (mm), SE = 62.96; 2D condition: Mean = 755.00 (mm), SE = 74.33; p < .01, Bonferroni-Holm corrected). •There was a significant difference between the OFF and 3D conditions (OFF condition: Mean = 403.67 (mm), SE = 26.07; 3D condition: Mean = 1046.67 (mm), SE = 62.96; p < .001, Bonferroni-Holm corrected), and between the OFF and 2D conditions (OFF condition: Mean = 403.67 (mm), SE = 26.07; 2D condition: Mean = 755.00 (mm), SE = 74.33; p < .01, Bonferroni-Holm corrected)These results strongly suggest that stereoscopic displays enhance the visual presence and realism of virtual agents, thereby working to bring human spatial behavior closer to real-world social norms regarding interpersonal distance. Furthermore, these highlights that the mere presence of the agent influences spatial behavior, regardless of the type of display.On the other hand, RQ2 (ratings of discomfort and likability) showed no significant differences between the 3D and 2D conditions (Discomfort; 3D condition: Mean = 3.36, SE = 0.16; 2D condition: Mean = 3.18, SE = 0.15; p = .27, n.s. , Bonferroni-Holm corrected, Likeability; 3D condition: Mean = 2.51, SE = 0.13; 2D condition: Mean = 2.48, SE = 0.12; p = .88, n.s., Bonferroni-Holm corrected). This result suggests that in a dynamic task such as walking, the display format alone may not be the main driver of emotional responses. It is possible that participants' attention was distributed throughout the environment, or other factors, such as the passive behavior of the agent, had a greater impact on perceived comfort and likability than nuances in display depth.This study shows that virtual agents displayed on a stereoscopic screen have a significant effect on interpersonal distance in dynamic situations, bringing interactions closer to natural human social behavior. This finding is an important step towards achieving more natural and comfortable interactions with on-screen virtual agents, and will contribute greatly to the development of augmented reality (AR), virtual reality (VR) and mixed reality (MR) simulations in particular.This study has limitations: small sample size (15 Japanese participants). Future work needs diverse groups and varied agent behaviors (e.g., dynamic gaze) for more natural interactions.

Tomoya Minegishi

Conference Proceedings

Exploring Virtual Reality for Drone Pilot Training: A Study on Japanese Certification Tasks with RealFlight

This study explores the potential of using Virtual Reality (VR) for supporting the Japanese second-class unmanned aircraft remote pilot certification training, by analyzing its effectiveness for development of maneuvering skills in the certification tasks. An experiment was conducted with six participants taking a drone school course, in which a flight simulator was used for the first day of the training. Instructors could monitor the participant’s view and, in addition, freely navigate the scenario from a controllable perspective, while flight path data was recorded. Using a simulation approach allowed instructors to monitor flight performance, identify motor skill issues, and use tools that helped them provide tailored feedback. Also, having independent views for the instructor and the participant made the guidance and correction comparable to traditional training. Despite variation in simulator performance, all six participants successfully passed the final certification exam. This outcome suggests that the VR training does not have a negative impact on the exam performance. Also, it might provide additional pedagogical value by highlighting and quantifying difficulties that are less apparent in live training environments. This study demonstrates that VR-based simulation can complement conventional training for Japan’s second-class drone certification. The results also suggest that the use of this technology is worth exploring for training more complex tasks. Despite variation in simulator performance, all six participants successfully passed the final certification exam. This outcome suggests that the VR training does not have a negative impact on the exam performance. Also, it might provide additional pedagogical value by highlighting and quantifying difficulties that are less apparent in live training environments. This study demonstrates that VR-based simulation can complement conventional training for Japan’s second-class drone certification. The results also suggest that the use of this technology is worth exploring for training more complex tasks.

Juan Sebastian Ruiz Medina, Sungju Maeng, Nianzhi Tu, Makoto Itoh

Conference Proceedings

Design and structure of sightseeing verbal maps：A case study in Shanghai, China.

Enabling independent mobility for visually impaired individuals is crucial for fostering social inclusion and enhancing quality of life. While existing electronic travel aid systems often rely on pre-installed roadside infrastructure or require prior physical visits to unfamiliar locations, such dependencies significantly restrict real-world adaptability and accessibility. To overcome these limitations, this study proposes a novel enabling technology: an at-home simulation system based on sightseeing verbal maps (A comprehensive breakdown of the sightseeing verbal maps' structure will be presented in the main text). By integrating spatially encoded audio guidance with context-rich environmental sounds, the system's video component synchronously presents dynamic video content that visually simulates the intended navigation route. This combined audiovisual tool is designed to offer highly immersive virtual reconstructions of outdoor environments, providing scalable and accessible preparatory training that supports spatial learning and navigational confidence. This preliminary study adopted a qualitative research design to evaluate a prototype system that was originally developed in Japan but was linguistically and culturally adapted for Chinese users. Through purposive sampling, seven Chinese international students residing in Japan were recruited. Data collection involved remote semi-structured interviews conducted via Zoom, each lasting approximately 60 minutes. Interview transcripts underwent thematic analysis to identify key patterns related to (1) cross-cultural usability challenges of the adapted system, (2) behavioral adaptation strategies in unfamiliar environments, and (3) user perceptions of simulated mobility training scenarios. Three central themes emerged from the analysis: navigation accuracy, cultural engagement, and sound design. Participants emphasized the importance of precise and timely auditory instructions for effective wayfinding, while also highlighting the need for culturally relevant points of interest and contextualized soundscapes that enhance environmental awareness and emotional comfort. These findings underscore the necessity for culturally adaptive design in auditory interfaces and context-rich narration within sightseeing verbal maps. This study represents the first step toward cross-cultural validation of sightseeing verbal maps beyond Japan, demonstrating their feasibility and acceptability for Chinese users and urban contexts. It addresses a critical gap in the localization of assistive technologies for visually impaired populations. Focusing on Chinese international students provided valuable insights into transnational accessibility needs during the system’s early development phase. Although limited by a small and region-specific sample, the study establishes a foundation for future work that should include expanded cross-cultural testing, diverse user groups, and technical optimization of audio balancing to improve performance under real-world noise conditions.

Tianyu Li, Takashi Uchida

Conference Proceedings

Analyzing Resource Performance in a 3D Virtual Immersive Environment

This paper presents a comprehensive approach to performance testing in 3D virtual reality applications developed using Unity, with a focus on continuous evaluation and optimization throughout the development process. The methodology started with an immersive 3D application over four versions and used native tools from the Unity engine (Profiler) to measure specific performance metrics, such as CPU, GPU, memory, audio, video, physics and UI usage. This approach allowed for early identification of problems (bottlenecks) and continuous optimization of the application at each stage of development. The main results include significant improvements in memory usage, reduction in the number of batches and triangles rendered, and adjustments to the physics that resulted in improvements in the frame rate. The study also highlights the importance of balancing visual fidelity with performance optimizations, and that continuous performance testing is essential to create optimized immersive applications, emphasizing the importance of initial profiling and iterative improvements.

Gilberto Oliveira Neto, Ana Caroline França, Anderson Oliveira

Conference Proceedings

Designing Persuasive Interactions with Pet-Type Virtual Agents: Effects of Emotion and Context in Mixed Reality

Persuasive technology research has increasingly examined how computational systems can encourage behavioral change, with applications ranging from health management to education and sustainability. While many approaches have focused on text-based messages, visual prompts, or gamification techniques, a growing body of work emphasizes the role of emotionally expressive agents that interact with users in more natural and embodied ways. Pet-type agents in particular offer unique potential, as their familiar and socially accepted forms can elicit empathy and trust. In everyday life, people readily interpret the intentions of pets through gestures such as approaching, barking, or pointing with their gaze, making this a promising model for persuasive design. However, the combined effects of emotions and specific action strategies on persuasion remain underexplored, especially in immersive environments.Building on our prior findings that sadness expressed by a four-legged agent could effectively promote compliance while happiness and anger were often misinterpreted, this study investigates how emotional expressions and behavioral cues interact to influence persuasion in a mixed reality (MR) environment. We developed a virtual pet dog that combined four types of emotional expression—sadness, happiness, anger, and neutral—with three categories of behavioral action: attention calling (e.g., approaching, barking, making eye contact), guiding (moving toward a target location), and pointing (alternating gaze between an object and its destination). These combinations were applied across a variety of everyday contexts, including pet-related tasks such as feeding and tidying toys, non-pet-related tasks such as reading, putting away books, and waste disposal, behaviors to be limited such as smartphone use, and emergency warnings such as moving outside a room. Two experimental conditions were compared: immersive MR interaction using HoloLens 2 and video-based presentation of the same persuasive behaviors.The results revealed that combinations such as sadness with pointing were perceived as supportive and effective in encouraging compliance, while anger with guiding sometimes evoked discomfort. Video conditions achieved higher success rates for visually straightforward tasks such as reading or feeding, whereas MR conditions highlighted the importance of interactivity, as participants expected the agent to respond contingently to their actions. Subjective reports indicated that MR participants viewed the agent less as a visual prompt and more as a social partner, leading to expectations for dialogue and responsiveness.These findings suggest that persuasive design in MR requires not only appropriate emotion–action pairings but also mechanisms for interactive responsiveness. By clarifying the role of emotional and behavioral cues in daily contexts, this study contributes to human-centered design by providing guidelines for persuasive agents that support habit improvement in everyday life. The implications extend to applications in education, healthcare, and eldercare, where virtual companions may offer scalable, engaging, and socially acceptable means of encouraging positive behaviors.

Kaoru Sumi, Rio Harada

Conference Proceedings

Semantic Segmentation-Guided 3D Shape Reconstruction of Indoor Scenes Using a PointNet-Based Autoencoder

This study aims to automatically construct virtual spaces that faithfully reflect the geometry and object arrangement in real-world environments. As a first step, we proposed a method for the three-dimensional (3D) shape reconstruction of indoor scenes using a PointNet-based autoencoder guided by semantic information. The proposed method first segmented a 3D point cloud into semantic classes and then applied a separately trained autoencoder to each class. To validate its effectiveness, we used the ScanNet++ indoor scene dataset and our own real-world data captured using a 3D scanner, performing qualitative visual comparisons and quantitative evaluations using metrics such as Chamfer distance (CD) and Earth mover’s distance (EMD). The results demonstrated that the proposed method achieved high visual fidelity and low CD error (4.23 × 10⁻⁴) on validation data similar to the training set. Although point scattering was observed in the unseen test data, the reconstruction fidelity still showed a clear improvement over prior work. Furthermore, we analyzed the counterintuitive observation that EMD showed an opposite trend to CD and showed that this was a statistical effect arising from the difference in the number of instances used for evaluation. A potential application of this method was also identified: by limiting the target classes, furniture could be intentionally excluded and only the skeletal structure of the space could be reconstructed. Future work will explore enhancing the local feature representation by adding normal information as an input feature and improving robustness through post-segmentation noise removal.

Takahiro Miki, Yusuke Osawa, Keiichi Watanuki

Conference Proceedings

Providers' perceptions of the challenges associated with Virtual Reality use in lung cancer care

Lung cancer patients face challenges when seeking information to make decisions in the treatment phase of their journeys. Although many technologies have been explored in supporting them in decision-making, little has been investigated when it comes to the potential of Virtual Reality in supporting their treatment preparedness. This study collected healthcare providers’ perceptions on the use of VR in addressing lung cancer patients’ treatment-related challenges. Interview data was collected, and providers expressed their concerns as it related to the tools (cost, accessibility, and customization), the systems (implementation in the workflows, and inadequate training), the people (resistance to new technologies, comfort using new tools, more load on providers, and fewer communication channels). To ensure safe and effective implementation of VR use in lung cancer care, these challenges should be addressed.

Safa Elkefi

Conference Proceedings

Augmented Reality for Manual Manufacturing Operations: Training, Assistance, and Usability Evaluation

Digitalization represents a strategic enabler of innovation in industrial processes, particularly within complex and safety-critical domains. In the transition toward Industry 4.0, Augmented Reality (AR) demonstrates considerable potential to reshape work organization by enhancing efficiency and ensuring higher product quality. AR technologies can provide operators with context-aware instructions, interactive visual guidance, and improved operational control during cognitive and technically demanding tasks, such as electrical wiring assembly in the aerospace sector. To investigate the actual potential of video see-through devices, a dedicated AR application was developed for Meta Quest 3. The case study encompassed the digitization of technical documentation, the integration of three-dimensional models, and the design of an interactive User Interface (UI). The Usability of the application and its impact on Cognitive Load (CL) were assessed through controlled laboratory experiments involving two groups: one employing Meta Quest 3 and the other relying on conventional paper-based documentation. The results provide empirical evidence on the practical relevance and limitations of video see-through AR in supporting assembly operations. Specifically, the study revealed that the video see-through mode of Meta Quest 3 presents certain limitations and imposes a higher CL on users, although it also elicited high levels of user satisfaction regarding the use of AR technology. These results underscore the necessity of further optimizing AR hardware and interaction design to mitigate cognitive demands, while confirming the promising role of AR in advancing industrial training and assembly processes.

Francesca Massa, Dario Farese, Giuseppe Di Gironimo, Andrea Tarallo

Conference Proceedings

Inclusion Through Sound: A Systematic Review of Spatial Audio, Sonification, and Interaction Design in Immersive Technologies for Blind and Visually Impaired Users

Immersive technologies such as virtual and augmented reality increasingly depend on sound-based interaction. For blind and visually impaired (BVI) users, audio-first design has become not an enhancement but a requirement, enabling orientation, navigation, training, and equitable participation in environments that are otherwise inaccessible. This review synthesizes nearly 1,900 works retrieved from OpenAlex and Crossref through a reproducible Python-based pipeline, which was further refined through thematic classification into six major domains: spatial audio and head-related transfer functions (HRTFs), assistive technologies for navigation, sonification and auditory displays, auditory cognition, immersive system design and evaluation, and inclusive design frameworks. The analysis reveals that spatialized audio and multimodal interfaces consistently enhance presence and reduce workload, yet the field continues to face unresolved challenges, including the absence of scalable methods for personalizing HRTFs, the lack of unified evaluation standards for auditory interaction, and limited integration of accessibility frameworks into immersive design pipelines. This review provides an updated state-of-the-art synthesis, identifies underexplored questions, and highlights the necessity of embedding inclusion into immersive sound research. The methodological contribution of a transparent and extensible Python pipeline ensures the reproducibility of this review and establishes a foundation for ongoing meta-analysis in sound interaction and accessibility research.

Daniel A Muñoz

Conference Proceedings

Can LLMs assist in job interview preparation? Assessing the quality and effectiveness of LLM-generated feedback

Large language models (LLMs) have demonstrated strong reasoning capabilities, making them potential candidates for generating formative feedback in learning contexts. This paper evaluates the ability of LLMs to provide formative feedback on interviewees' responses in a job interview task. Specifically, the degree of explanation in an interviewee’s response, a key communication skill, was used as the focal assessment criterion. Combinations of LLM models (i.e., GPT-3.5-Turbo, Gemini-1.5-Pro) with various chain-of-thought (CoT) prompting strategies, including task definition, domain knowledge, and contrastive prompting, are examined across multiple self-reported metrics of feedback quality effectiveness. Data was collected from 663 participants on Amazon Mechanical Turk using a between-subjects design with six experimental conditions, each corresponding to a combination of LLM model and prompting strategy. Results indicate that users perceived LLMs as having a moderate ability to provide formative feedback for job interviews, though the feedback was at times viewed as irrelevant or potentially harmful. The choice of LLM model and prompting strategy significantly influenced perceived feedback quality, with GPT-3.5-Turbo generally rated more favorably than Gemini-1.5-Pro. While stronger task performance occasionally aligned with higher user ratings, the relationship between performance and perception was not strictly linear. These findings are discussed in terms of design implications for enhancing the quality and effectiveness of LLM-generated feedback in interview training contexts.

Ghritachi Mahajani, Amir Behzadan, Theodora Chaspari

Conference Proceedings

Leveraging AI and Multivariate Analysis to Convert Product Requirements into Product Specifications

The objective of this work is to enable designers to easily determine the optimal product dimensions to accommodate diverse user populations. Artificial intelligence (AI) is integrated with data on a population’s body size and shape (anthropometry) and a custom analysis function to convert natural-language design requirements to technical design specifications. This leverages a strength of AI to help designers to overcome the challenges of unfamiliarity with anthropometric terminology. Simultaneously, it mitigates some limitations of AI by performing the analysis in an environment specifically designed for this task. Ultimately, it allows human factors engineers and ergonomists to easily explore design trade-offs in a multivariate design space.In human factors and ergonomics, considering a wide range of anthropometry is essential to ensuring that products are usable and safe. However, the process of determining the dimensions of a product to fit a target percentage of the population is often challenging for designers, particularly when dealing with multiple product dimensions (multivariate design). Unlike univariate design, which typically focuses on optimizing a single dimension, multivariate design involves balancing multiple variables, each affecting the accommodation in different ways. While some tools for assisting in multivariate design exist, such as the Multivariate Anthropometry Testing Tool from the Human Factors and Ergonomics Society (HFES), they necessitate an anthropometry-focused approach. This approach requires designers to identify the relevant anthropometric measures for each product variable and understand each measure’s relation to the product.Artificial intelligence (AI) has great potential in making human-centered design more accessible for designers. Current Generative Pre-trained Transformer (GPT) models can identify public datasets, such as the ANSUR II data, which contain detailed anthropometry from military personnel. The models are also able to match product dimensions to relevant anthropometric measures, inferring the relationships between a design variable and the associated anthropometry. However, the existing models struggle in two critical ways. First, they fail to reliably extract information from online datasets and often report incorrect data for design recommendations. Second, they are unable to conduct multivariate analyses. When asked to size a product in more than one dimension, the current models will report a series of independent univariate solutions, which is known to overestimate overall accommodation.To address these challenges, this work introduces the incorporation of function calling in GPT to improve multivariate accommodation analysis. Function calling allows GPT to trigger a backend process that directly accesses ANSUR II anthropometric data and accurately computes accommodation based on multiple dimensions. Because the function correctly performs multivariate analyses, the GPT is able to provide several design recommendations that meet the overall target, allowing the designer to select the most appropriate one. By allowing the GPT to infer user intent and adjust function arguments, this approach overcomes key limitations of existing tools and GPT models, enabling more efficient and accurate design solutions.

Zoe Marazita, Matthew Parkinson, Jessica Menold

Conference Proceedings

Can Voices Predict Emergency Severity? An Exploratory Analysis of EMS Calls

Emergency medical services (EMS) rely heavily on verbal communication during the initial phase of response. Dispatchers are trained to assess urgency based on both the content and tone of callers' voices. However, the potential to objectively estimate patient severity based solely on acoustic features of the caller’s speech has not been fully explored. This study investigates the feasibility of classifying patient severity—specifically distinguishing between "critical" and "non-critical" cases—using non-linguistic features derived from emergency call audio.We utilized real-world emergency call recordings provided by the Tokyo Fire Department. Each audio sample was labeled as either “critical” or “non-critical” based on post-response evaluations by emergency personnel or medical institutions. Acoustic features were extracted from the callers’ speech, including fundamental frequency (pitch), speech rate, jitter, intensity, and mel-frequency cepstral coefficients (MFCCs). No textual content was analyzed in this study; we focused exclusively on paralinguistic and spectral aspects of speech.Using the extracted features, we trained classification models including logistic regression, support vector machines (SVM), and random forest classifiers. Feature selection and dimensionality reduction techniques, such as recursive feature elimination and principal component analysis (PCA), were applied to optimize model performance and identify key indicators. Classification performance was evaluated using standard metrics, though specific numeric results are omitted here due to the exploratory nature of the study.Our findings indicate that certain vocal characteristics—such as elevated pitch, increased speech rate, and variability in vocal intensity—were more frequently observed in calls associated with critical cases. These patterns may reflect psychological urgency, stress, or heightened emotional states in the caller, indirectly signaling the severity of the patient’s condition.This study demonstrates the potential of voice-based severity estimation as a supplementary tool for EMS dispatchers. By integrating such acoustic analysis into the triage process, emergency services may be able to support faster and more informed decision-making, especially in high-pressure environments where every second counts. The approach may also contribute to more effective resource allocation by identifying high-priority cases earlier in the response timeline.However, several limitations must be acknowledged, including the relatively limited size of the dataset, variability in recording conditions, and the influence of caller demographics and emotional disposition. Future work will aim to expand the dataset, incorporate automatic speech recognition (ASR) for combined linguistic and acoustic analysis, and explore deep learning-based classification models for improved generalization and robustness.This exploratory research provides foundational insights into the integration of AI-based acoustic analysis into emergency response workflows and highlights its potential to enhance the speed and accuracy of prehospital assessments.

Kanji Okazaki, Keiichi Watanuki

Conference Proceedings

Threading the Future: AI, 3D Technology, and Human Skills in the Evolving Fashion Workforce

Technology, particularly automation and artificial intelligence (AI), is fundamentally reshaping the future of work, impacting job roles, skill requirements, and the overall workplace environment. While some jobs may be displaced by automation, new roles are emerging, particularly those that require uniquely human skills such as creativity, critical thinking, emotional intelligence, and complex communication. The human element remains indispensable. Navigating this evolving landscape requires commitment to lifelong learning, upskilling, and embracing a mindset that views human-machine collaboration as essential rather than optional.Before the COVID-19 pandemic, the fashion industry had already begun experimenting with sustainable practices in production, including recycling, eco-conscious packaging, and environmentally responsible manufacturing and cleaning processes. Abbate, Centobelli, Cerchione, Nadeem, and Riccio (2024) observed that the textile, apparel, and fashion (TAF) industry “contributes significantly to global environmental pollution” (p. 2837). In response, both global brands and independent designers began adopting practices aimed at reducing environmental harm while also raising consumer awareness. Sung and Woo (2019) described slow fashion products as not only environmentally friendly but also socially and ethically responsible. Despite these efforts, fashion-related higher education programs have been slow to adapt their curricula to meet these new sustainability and technological standards.In recent years, and particularly since 2020, the fashion industry has rapidly embraced 3D digital technology as both a sustainability solution and a means of increasing efficiency. The shift toward digital fashion accelerated during the pandemic as businesses scrambled to maintain production with minimal human contact. 3D fashion design software, such as Browzwear and CLO, has revolutionized the design process by reducing the need for physical materials and eliminating wasteful prototyping. Designers can now build garments in a 3D environment, fit them on virtual avatars, and make instant alterations, allowing for quicker approvals and reduced shipping and sample costs. Dou (2024) points out that digital tools are optimizing not only prototyping and design reviews but also production and online merchandising, drastically shortening development timelines.As a result, entirely new job categories are emerging at the intersection of fashion and technology. Designers now need to master digital design tools rather than traditional sewing machines. This transformation is creating opportunities in AI-assisted design, virtual fitting, and sustainable tech innovation within a $3 trillion global industry. There is a growing demand for hybrid roles that blend creative, technical, and digital expertise. As fashion businesses transition deeper into the AI landscape, the integration of human-centered technology will be crucial, not just for productivity, but also to ensure ethical design practices and sustainability goals are met.The fashion industry's future will be shaped by how well it balances automation with human ingenuity. To remain relevant, educators, professionals, and companies must invest in forward-thinking, tech-integrated, and sustainability-driven practices that prepare the next generation for a rapidly changing workforce.ReferencesAbbate, S., Centobelli, P., Cerchione, R., Nadeem, S. P., & Riccio, E. (2024). A systematic literature review on sustainable practices in the textile, apparel and fashion industry. Journal of Cleaner Production, 433, 138257. https://doi.org/10.1016/j.jclepro.2023.138257Dou, Y. (2024). Digital transformation in fashion: The role of 3D technologies in sustainable product development. International Journal of Fashion Design, Technology and Education, 17(1), 45–58. https://doi.org/10.1080/17543266.2023.2265487Sung, K., & Woo, J. (2019). Consumers’ value–behavior gap in sustainable apparel consumption: An exploratory study of Korean consumers. Sustainability, 11(19), 5381. https://doi.org/10.3390/su11195381

Jenifer Roberts, Sandra Bailey

Conference Proceedings

Knowledge Graph-Enhanced Large Language Model Framework for Privacy-Preserving Document Processing in the AEC Domain

Data privacy and safety are critical concerns for companies in the Architecture, Engineering, and Construction (AEC) domain, which routinely handle sensitive textual data such as design criteria, project specifications, and compliance records. Protecting this information is vital for maintaining competitive advantage, meeting legal requirements, and ensuring safety and accountability. However, processing such domain-specific data is challenging. Rule-based systems require extensive manual rule sets, while supervised machine learning models need large, annotated datasets - both of which limit scalability and applicability in AEC contexts. Recent advances in large language models (LLMs) offer a promising alternative due to their ability to perform natural language tasks with minimal supervision. Yet, general-purpose LLMs pose two major concerns: they may generate inaccurate or irrelevant outputs on technical content, and their reliance on online services introduces significant privacy risks. To address these issues, this paper proposes a knowledge graph-enhanced LLM framework designed for local, privacy-preserving processing of sensitive AEC documents. Using the 2015 International Building Code (IBC) as an example, the framework operates in two stages. First, an LLM converts selected IBC chapters into a structured knowledge graph with 234 entities, 131 relationships, and 8 communities. Second, another LLM retrieves relevant context from the graph to generate accurate query responses. The system employs open-source models - nomic-embed-text for text embeddings and deepseek-r1 for context retrieval and generation. Evaluation using 661 query-answer-context records showed an average semantic similarity score of 0.83 and an average answer relevancy score of 0.71, indicating high accuracy and contextual alignment. The system runs entirely on a standalone machine, preserving full data privacy and incurring no cost. This work demonstrates a secure and effective approach for using LLMs in privacy-sensitive, domain-specific applications and lays the foundation for broader adoption in similar fields.

Fan Yang, Hazar Nicholas Dib, Jiansong Zhang

Conference Proceedings

Implementation of Artificial intelligence (AI) in Transport Accident Investigations

Transport accident investigations are crucial for understanding causal factors, improving system safety, and preventing future incidents. Traditionally, these investigations rely on a multidisciplinary process involving human expertise, manual data analysis, and narrative reconstruction. However, with the growing complexity of transportation systems and the increasing volume of operational data—from flight data recorders, cockpit voice recordings, sensor logs, to surveillance systems—the limitations of manual analysis are becoming evident. This paper explores the emerging role and potential of Artificial Intelligence (AI) in augmenting and transforming transport accident investigations across aviation, maritime, rail, and roadway domains.AI technologies such as machine learning, natural language processing (NLP), and computer vision are proving to be powerful tools in extracting patterns, identifying anomalies, and drawing correlations from large datasets that are otherwise time-consuming and error-prone for human analysts. This paper examines several case studies and research projects where AI-assisted tools have been piloted or implemented in post-accident analysis. These include automated speech recognition for cockpit voice recordings, anomaly detection in flight trajectories, and sentiment analysis of maintenance logs. Findings indicate that AI can significantly reduce investigation timeframes, increase objectivity in evidence evaluation, and uncover hidden contributing factors—particularly in cases involving complex system interactions or human-machine interface failures.Despite its promise, the implementation of AI in accident investigations is not without challenges. One critical concern is transparency and explainability. Unlike traditional analytical methods, AI models—especially deep learning systems—can function as "black boxes," making it difficult for investigators, regulators, and courts to interpret how a conclusion was reached. This raises questions about the admissibility of AI-generated evidence and its alignment with legal and ethical standards in safety investigations. The paper emphasizes the need for human-in-the-loop approaches where AI augments, rather than replaces, expert judgment. Human oversight remains essential in contextual interpretation, ethical reasoning, and final decision-making.Furthermore, the integration of AI into accident investigation agencies requires cultural and organizational shifts. Investigators need training not only in technical AI tools but also in data literacy, interdisciplinary collaboration, and understanding the biases that AI models may inherit from their training data. This paper proposes a roadmap for implementation, including phased adoption, validation protocols, inter-agency cooperation, and regulatory support.In conclusion, AI has the potential to revolutionize transport accident investigations by enhancing speed, depth, and predictive capability. However, its integration must be guided by principles of transparency, accountability, and collaboration between technologists and human factors experts. As transportation systems evolve toward greater automation and data dependence, leveraging AI in accident investigations is not only beneficial but essential for ensuring the continued integrity and learning capacity of safety-critical systems.

Dimitrios Ziakkas, Debra Henneberry, Ioanna Lekea

Conference Proceedings

Toward AI-Ready Graduates: Connecting Educational Innovation to Aviation Workforce Needs

Artificial intelligence (AI) and smart technologies are rapidly transforming the landscape of aviation/ aerospace technical education, raising critical questions about how academic programs can better prepare the workforce to meet evolving industry demands. This paper features insights from an initial, high-level investigation to better understand AI-related competencies in specialized aviation and aerospace fields. Initial observations indicate evolving and discipline-specific needs, particularly the applied skills identified as essential in aviation safety, cybersecurity, aeronautical sciences, and uncrewed and autonomous systems operations. In aviation safety, AI is increasingly used for predictive analytics, large-scale qualitative data processing, and data fusion to improve risk analysis. Integrating AI into safety-critical systems also introduces new challenges, including the need for updated certification processes, clearer understanding of AI limitations and failure modes, and the impact on traditional system safety practices. In the cybersecurity domain, ongoing work explores the use of AI and machine learning to detect anomalies and potential cyber events across vast datasets, including those generated from aircraft logs and manufacturing systems. Aeronautical sciences offer opportunities for AI to enhance operational decision-making, flight deck support, and maintenance forecasting through advanced data capture and analysis. In uncrewed and autonomous systems, AI technologies, including machine learning and agentic systems, improve human-system interoperability and enable increasingly autonomous capabilities. Across all areas, the study underscores the human factors challenge of AI interpretability, ensuring that AI-driven insights are transparent, explainable, and actionable, especially within safety-critical contexts. This research contributes a foundation for future curriculum development, aligning technical skill-building with operational realities and helping translate emerging technologies into effective, practice-ready educational experiences that meet both student and industry needs.

Brent Terwilliger, Mark Miller, Kristine Kiernan, David Harvie

Conference Proceedings

AI Integration for Bridging Advanced Analytics and Business Value in a Regulated World

The integration of Artificial Intelligence (AI) and advanced analytics into enterprise strategypromises substantial business value, from improved decision-making to new product innovation.However, realizing this potential is challenging, especially in highly regulated industries wherecompliance, data governance, and ethical constraints are paramount. This paper examines howorganizations can effectively bridge advanced analytics and tangible business value in a worldincreasingly defined by strict regulations. We present a comprehensive review of literature on AIadoption, value creation, and governance, identifying key enablers and barriers to successful AIintegration. A qualitative methodology is employed, synthesizing insights from academicresearch and industry case examples to outline best practices for aligning AI initiatives withbusiness objectives while upholding regulatory compliance. The results and discussion highlightcritical success factors, including strategic alignment of AI use-cases, robust data management,cross-functional governance frameworks, and a culture of responsible innovation. We find thatbalancing agility and compliance is essential: companies must innovate with AI under carefuloversight to avoid legal pitfalls and maintain stakeholder trust. This study contributes anintegrated perspective on deploying AI for business gain in regulated environments and proposesa roadmap to guide organizations. Conclusions emphasize the need for ongoing adaptation, asevolving regulations (such as the EU AI Act) and emerging technologies (like generative AI) willshape future integration efforts. Future work should explore longitudinal case studies to quantifyvalue realization and refine governance models for the next generation of AI solutions.

Dmytro Smirnov, J Cecil

Conference Proceedings

Making Legal Decisions and Managing Legal Risks in an AI World: A Better Approach

Advances in AI have propelled it to the forefront of applications efforts in many different fields, including legal decision-making and managing legal risks. Assertions are made that AI tools can used to analyze past case outcomes, judicial rulings, and legal precedents to predict the likely trajectory and outcomes of litigation. In this paper, we will present the results of our search for evidence of actual use, along with an evaluation of the strengths and weaknesses of such use.However, there are fundamental conceptual and practical limitations to this approach, including the fact that almost all cases are resolved through settlement with a resolution that remains confidential and is not publicly reported. Further, use of AI for legal decisions raises significant risks inherent to the way AI works (e.g., algorithmic bias, lack of transparency, etc.) and ethical, regulatory, and due process concerns. A better approach is to use AI for the purposes it is eminently suitable and superior for and to use Decision Analysis for making decisions in risky, complex situations like managing legal risks and making legal decisions. Decision Analysis was developed over 50 years ago for making better decisions in complex and risky situations and is standard and required in a number of high-stakes, high-risk industries like drug development and oil and gas development. However, its application for legal decision-making and risk management is very limited for reasons discussed in this paper. We present an approach for understanding and applying AI and Decision Analysis appropriately in the legal arena, including a survey of current AI and Decision Analysis analytical tools for legal. This paper will build upon the conceptual foundation described in (reference to 2021 ICIA paper) and present a practical and theoretically sound approach for integrating AI and Decision Analysis in future technology for decision making and risk management in general and, in particular, in the legal arena.

John Celona, Olof Heggemann, Adam Seiver

Conference Proceedings

Acceptance of AI in the workplace: Literature analysis and process-oriented methods to foster organizational acceptance and trust of AI

The use of artificial intelligence (AI) in manufacturing can increase productivity, improve decision-making, reduce routine tasks, and enhance both workplace safety and job satisfaction. However, our studies indicate that employees without managerial roles often express reservations toward AI, while managers identify acceptance as a major obstacle. The aim of this paper is therefore to identify interventions that foster acceptance of AI in manufacturing. A systematic literature review was conducted following the PRISMA approach. Out of 295 initially identified publications, nine were analysed in detail using qualitative content analysis based on Mayring. This process yielded twelve categories with a total of 44 interventions, which were mapped onto a process-oriented model for fostering acceptance and trust. The interventions focus strongly on the early stages of implementation and emphasize qualification, demonstration of added value, participation, internal communication, cooperation, and corporate culture. While technical characteristics of AI systems remain relevant, they are outweighed by human and organizational factors. The findings suggest that companies should not rely solely on technical solutions but must also invest in employees, communication, and culture to secure sustainable acceptance.

Jennifer Link, Sascha Stowasser

Conference Proceedings

Artificial Intelligence and the future of work in the construction industry: A systematic review

Artificial Intelligence (AI) has been adopted in the construction industry for multiple tasks. While it was identified that AI has critical impacts on the future of work, few studies have explored the construction workforce. Existing studies mainly discussed the applications of AI on construction tasks, while a comprehensive review of how AI will influence the future of work in this sector is still lacking. To address the gaps, this study conducts a systematic review to investigate how AI will influence the future of work in the construction industry. Based on over 500 articles, 20 studies from 2020 to 2025 were reviewed to identify current trends and challenges in this field. Key findings reveal that current research trends include construction operations and AI, construction workforce education and AI, construction industry development and AI, as well as AI and relevant technologies. It was indicated that AI will not replace the labor workforce but will redefine some existing roles and create new roles in the construction industry. Main challenges include AI-related skills and training to develop the AI-skilled workforce, human-centered AI that emphasizes human-AI collaboration, and integration of AI and relevant technologies in the construction industry. Future studies could focus on human-centered AI development, AI-related training and education, and construction industry development with AI. Companies should pay attention to AI-related training, adjustment of roles, and transparent communication. This study provides a comprehensive understanding of how AI could reshape the future of work in the construction industry and reveals future research directions, contributing to both workforce development and AI integration in this sector. The recommendations could also help foster a balanced, human-centric AI adoption process in the construction industry.

Hongyue Wu

Conference Proceedings

Toward Climate-Smart Agriculture: An Environmental, Social, and Governance (ESG) -Centric Edge AI Architecture for Smallholder Farmers

This paper introduces Edge Fluent, a novel Environmental, Social, and Governance (ESG)-oriented Internet of Things (IoT) edge architecture designed to empower smallholder farmers in the agri-dairy sector through inclusive artificial intelligence (AI). Central to the system is a fine-tuned, multi-label DistilBERT model deployed on low-power, resource-constrained edge devices, enabling real-time ESG classification, multilingual translation of regulatory content, and actionable intervention support. By addressing the pervasive barrier of language accessibility—particularly among non-English-speaking and low-literacy farming communities—the platform ensures equitable delivery of ESG intelligence and climate-resilient decision-making.Validated through live deployments on dairy farms equipped with Class 10 veterinary sensors and farmer interfaces in native dialects, the solution facilitates methane emission tracking, rumination-based health monitoring, feed optimization, and Scope 1–3 emissions traceability. Designed for offline inference and multimodal sensor inputs, the system reinforces ESG compliance, sustainable certification, and data harmonization across distributed farm networks. Ultimately, this architecture advances UN Sustainable Development Goals (SDGs) by embedding linguistic inclusion at the core of climate-smart agriculture and redefining sustainability through equitable technological integration.

Chandrasekar Vuppalapati, Anitha Ilapakurti, Shruti Vuppalapati, Sharat Kedari, Jaya Vuppalapati

Conference Proceedings

The Impact of Artificial Intelligence on Sustainable Regional Innovation Ecosystems and Participation

This study explores the transformative role of Artificial Intelligence (AI) within the context of Regional Innovation Ecosystems (RIVs), Smart Specialisation Strategies (RIS3), and the European Union’s Mission on Adaptation to Climate Change. Building upon ten empirical cases from nine countries in the Baltic Sea region, the research examines AI’s potential to enhance sustainable development, climate resilience, and inter-regional cooperation.While the literature on regional innovation emphasizes governance models, stakeholder engagement, and impact assessment, the specific contributions and limitations of AI within these frameworks remain underexplored. This study aims to fill that gap by analyzing the integration of AI across various components of regional development strategies.AI offers substantial benefits in climate adaptation through enhanced data processing capabilities. It improves climate modeling and forecasting by analyzing vast datasets from satellites, sensors, and historical records, enabling more accurate predictions of extreme weather events and strengthening early warning systems. AI supports the optimization of critical resources such as water, energy, and land, and informs the development of adaptive infrastructure including smart energy grids. It also aids in ecosystem conservation and enhances carbon capture through predictive modeling.In the domain of regional collaboration, AI facilitates partner identification and strategic matchmaking by analyzing regional strengths, competencies, and innovation needs. It supports multilingual knowledge-sharing platforms and provides real-time translation, thus lowering communication barriers and improving cross-border collaboration. AI-driven tools also enhance the Entrepreneurial Discovery Process (EDP) by delivering market trend analysis, identifying niche areas for specialization, and simulating policy outcomes to guide strategic decisions.When embedded in participatory processes, AI augments human-centric approaches by analyzing qualitative data from stakeholder workshops, identifying dominant themes, and offering real-time facilitation support. Furthermore, AI can support the creation of targeted communication materials, helping tailor regional development messages to different audiences.The benefits of AI integration include increased speed and efficiency in innovation processes, greater accuracy in modeling and assessments, enhanced foresight through pattern recognition, optimized resource allocation, and scalable solutions that can address both local and global challenges. AI also helps navigate the complexity inherent in multi-stakeholder ecosystems and climate governance.However, several disadvantages merit attention. High initial costs for infrastructure and skilled personnel can impede adoption, especially in less developed regions. The reliability of AI depends heavily on data quality; biased or incomplete data can lead to flawed outputs. Ethical risks include algorithmic bias, lack of transparency, and the “black box” nature of many AI systems. These concerns call for robust oversight and inclusive governance mechanisms. Additionally, AI cannot replicate human intuition, creativity, or value-based judgment, which are essential in participatory and strategic processes. Risks such as job displacement, cybersecurity threats, and the environmental impact of energy-intensive AI systems also raise critical concerns. Finally, there is a risk of widening the digital divide between technologically advanced and under-resourced regions.In conclusion, AI holds significant potential to accelerate sustainable regional innovation, smart specialisation, and climate adaptation. However, realizing these benefits requires a careful balance between technological advancement and social responsibility. Human agency, ethical oversight, and inclusive governance must remain central to ensure that AI contributes to equitable and resilient regional futures.

Taina Tukiainen, Minna Takala

Conference Proceedings

Generative AI-Driven Optimization for Cultural Packaging Design: Translating Chinese Poetic Imagery into Tea Packaging Design

The rapid advancement of AIGC has unlocked new potential in design processes, making it an essential tool for innovation. However, systematic research on AIGC-based product packaging design remains insufficient, particularly in enhancing design efficiency and accurately reflecting cultural elements. This study proposes an AIGC-based optimization framework to address these challenges. First, ChatGPT and the LDA model were used to extract imagery words from high-quality literary works aligned with the design theme. These words served as prompts for ChatGPT, guiding iterative image creation through Midjourney. Furthermore, AIGC-based image recognition was integrated to incorporate big data into the decision-making process. To ensure cultural relevance and consumer satisfaction, the AHP and FCE methods were employed to conduct a multidimensional evaluation and optimization. The empirical findings from the case study employing The Thousand Poems as thematic content for Jiangnan Longjing tea packaging design substantiate that AI-generated content driven design methodologies not only optimize decision-making efficacy but also establish an adaptive framework for design refinement, thereby providing a robust theoretical and practical foundation for subsequent scholarly inquiry.

Zixiao Chen, RongRong Fu

Conference Proceedings

Using Generative AI Personas to Study Consent in Educational Data Use: Views of Parents and Elementary School Students

Digital transformation in education (Educational DX) is expected to improve learning outcomes and school management. To achieve this, various stakeholders need to utilize learning data. These include health and life data (such as health records, fitness history, health insurance cards), learning activity data (attendance, study histories, online logs, test answers, portfolios, videos), survey and evaluation data (named or anonymous questionnaires, psychological tests, peer reviews), and personal or family information (parental occupation, home situation, scholarship forms). Because such data often includes personal information, permission is required. If the data are about children, parents also need to agree. Even when people understand that the data are helpful, they may hesitate when it involves themselves or their children.We explored this issue with a persona-based simulation using generative AI. With the GPT-4.0 model, we created virtual parents and elementary school students with diverse backgrounds. These personas responded to applications for data use. Applications varied in purpose, length of data retention, and whether data would be shared with others. We considered five types of applicants: (1) school administrators and boards of education, (2) researchers, (3) teachers, (4) parents of students, and (5) others.This abstract presents the results for school administrators and boards of education, focusing on this group because their role is essential for promoting Educational DX and responding to social expectations.Parents gave clear responses. For attendance records, 45 of 50 gave conditional approval. Most of these (38 cases) required anonymization, and only one parent refused. For study histories, 43 parents gave conditional approval. Their main conditions were retention and deletion rules (16 cases) and anonymization (17 cases). For named surveys, 39 parents gave conditional approval, but the reasons were different: educational usefulness (12 cases) and clarity of purpose (six cases). Anonymization was not requested, since names were essential. Refusals were rare and usually due to unclear purpose or little educational value.Students also showed strong patterns. For attendance records, 45 of 50 gave conditional approval, mentioning anonymization (14 cases) or educational usefulness (11 cases). None refused. For named surveys, 37 gave conditional approval, often saying they hoped the results would improve their school. Anonymization was not relevant here. By contrast, health insurance cards led to 28 refusals, with only five approvals and 17 conditional approvals. Students said the data felt “too personal” or they did not know how it would be used. For parents’ occupational information, 21 refused, two approved, and 27 gave conditional approval, often saying it was not connected to their own learning.In summary, parents focused on anonymization, retention rules, and a clear purpose. Students were more willing to help with school improvement, but strongly rejected data that felt private or unrelated. Conditions for approval differed by data type. Anonymization was central for attendance and study histories, while educational usefulness was decisive for named surveys.This study shows that conditional approval was the most common response. While this abstract reports only on school administrators and boards of education, results for other applicant types will be presented in the full paper.

Mitsuhiro Takasaki, Tetsuro Kakeshita

Conference Proceedings

Measuring group cohesion as a factor in collaborative decision making

We define group cohesion as a general consistency in group objectives or values over time that encompasses an overall persistence in the cooperative nature of the members’ interactions. This characterization applies primarily to task-oriented teams, although it may also be used more broadly to purpose-driven groups.We have developed a collection of AI tools that can estimate the degree of group cohesion by measuring the quality of interaction between group members. To compute this assessment, we identify a series of sociolinguistic behaviors, both individual and collective, that obtain within the group as reflected in the members’ utterances. Individual behaviors impacting group cohesion include agenda control, involvement, agreement and disagreement; relevant collective behaviors include the balance of agreement-disagreement, sociability, and task focus. Estimating distribution and degree of these behaviors are required to assess the character of members’ relations and the consistency of group’s objective. In this paper we focus on collective behaviors, and how they can be automatically computed from group interactions. Implementation of individual behaviors can be found in (Broadwell et al., 2013).One measure of group cohesion is a degree of task focus among the group members. It is a collective behavior that can be measured by the degree to which the discussion is focused on a shared objective, as well as by the efficiency with which the group works towards this objective. The efficiency of this progress is evidenced by the degree to which the discourse stays on topic with few off-topic digressions. Another measure of group cohesion is persistence of roles (Bales, 2001), which tracks whether certain key social functions in the group, such as leadership, persist throughout the discourse, though not necessarily filled continuously by the same individuals. A cohesive group is also characterized by a high degree of sociability. This includes adherence to general conversational principles, which are in turn reflected by certain sequences of dialogue acts, i.e., question-answer, offer-response, as well as sequences of expressions classified as conversational norms, including greetings, thanks and apologies. Groups with higher values of the sociability behavior are considered more cohesive.In this paper we discuss a practical implementation of an AI system that can determine cohesiveness of a group engaged in task-oriented discussion.* Bales, Robert Freed (2001) Social Interaction Systems, Theory and Measurement, Transaction Pub.* George Aaron Broadwell, Jennifer Stromer-Galley, Tomek Strzalkowski, Sarah Taylor, Umit Boz, Samira Shaikh, Liu Ting, and Nick Webb (2013) Modeling Socio-Cultural Phenomena in Discourse. Journal of Natural Language Engineering. 19(2): 213-257. Cambridge

Tomek Strzalkowski

Conference Proceedings

Secure Authentication Design For AI Agents

In the financial industry, artificial intelligence (AI) agents are increasingly adopted in order to drive higher productivity and financial performance. These solutions require access to critical enterprise systems like ERPs, trading platforms or other solutions where they need to authenticate and execute actions on behalf of their users. This is brings specific security challenges on how to reliably authenticate the agents to these critical systems. In this paper we will explore the common anti-patterns on how to design the authentication mechanism for agents in function calling use cases. Additionally we will provide the best solution for implementing the authentication and explore two alternative security solutions depending on the capabilities of the external system. Finally we will provide an example architecture of using the MCP protocol while authenticating the agent.

Anna Topol, Elizabeth Koumpan, Grzegorz Jurek, Laurentiu Ghergu

Conference Proceedings

Large language models in programming: a meta-analysis of tools, users, and human-computer interaction themes

Since 2021, the rapid integration of large language models (LLMs), such as OpenAI’s Codex and ChatGPT, into programming has reshaped how software is written, learned, and maintained. Tools such as GitHub Copilot, Amazon CodeWhisperer, Tabnine, and Sourcegraph Cody have evolved from experimental aids to core elements of modern workflows, while academic prototypes continue to explore new interfaces and teaching applications. This meta-analysis synthesizes empirical research, user evaluations, and product-level comparisons to provide a comprehensive view of the opportunities and challenges posed by LLM-based programming assistants. The analysis considers novice programmers, professional developers, researchers, and educators, highlighting recurring human-computer interaction (HCI) themes of trust calibration, cognitive load management, interface modalities, and the balance between automation and user control.The methodology followed a systematic review of studies published between 2021 and early 2025 in ACM, IEEE, arXiv, and other recognized repositories. Industry reports and tool documentation were included to capture emerging developments. A qualitative thematic synthesis integrated findings across varied research contexts, including user studies, classroom evaluations, and professional development workflows, revealing consistent patterns in tool use, learning outcomes, and professional practice, while also identifying gaps in current understanding.Novice programmers benefit from immediate feedback, reduced syntax errors, and increased confidence. Yet these advantages can foster over-reliance if tools are used as answer generators. Structured support, such as hint-based prompting and code validation, helps students engage more deeply with core concepts. Professional developers report productivity gains in routine tasks and code navigation but remain cautious about correctness, security, and workflow disruptions. Vulnerability checks, auto-generated tests, and explanation features are especially valued. Researchers and educators employ LLM-based programming tools to streamline analysis, generate assessments, and create interactive teaching methods, though concerns persist about equity, academic integrity, and responsible classroom use.Across all groups, four HCI themes stand out. trust calibration is essential to help users understand both strengths and limitations. Cognitive load management improves when tools integrate seamlessly into workflows and provide context-aware assistance. Interface modalities matter, with value in combining inline completions and conversational explanations to support varied scenarios. Finally, balancing automation with user control ensures accountability and promotes critical engagement, meaning that users remain actively involved in evaluating, verifying, and refining AI-generated outputs rather than passively accepting them.These findings show that LLM-based programming tools are not inherently harmful; outcomes depend on how they are used and designed. For learners, risks arise when practice is bypassed, limiting skill growth. For professionals, challenges involve accuracy, security, and workflow integration. Effective use treats LLMs as collaborators that support reflection and experimentation rather than replacements for human reasoning. Students benefit when tools provide hints and guidance instead of complete solutions, encouraging deeper understanding.In conclusion, LLM-based programming tools present strong potential for advancing productivity, education, and research. Benefits include faster coding, improved learning, and streamlined teaching. Persistent challenges remain related to correctness, cognitive load, and trust. Future research should emphasize longitudinal studies of skill development and design strategies that improve transparency, context, and pedagogy. Ethical and legal considerations, including attribution, privacy, and access, must also be addressed. By positioning these tools as collaborative partners, the computing community can maximize their benefits while reducing risks for developers, educators, and researchers.

Daniel Olivares, Charles Bennington, Abigail Skillestad

Conference Proceedings

Human-Centered AI for Automotive Systems: Towards Explainable, Intercultural, and Standardized Integration

The integration of intelligent systems into socio-technical environments requires not only technical excellence but also a systematic consideration of human, cultural, and organizational factors. This paper proposes a Human-Centered Artificial Intelligence (HCAI) framework tailored to the automotive domain, bridging the gap between international standards (e.g., Automotive SPICE, ISO 26262, ISO 9241-221) and the practical deployment of AI-enabled systems. The approach is based on three complementary dimensions: (1) Explainability and Transparency, ensuring that AI-supported decision-making processes are comprehensible to engineers, managers, and auditors; (2) Intercultural Design Integration, incorporating cultural user interface design principles to enhance acceptance in global development teams; and (3) Standardized Assessment, leveraging process models such as Automotive SPICE PAM 4.0 to establish consistent, auditable practices. The research employs context-augmented generation (CAG) techniques with local large language models to assess AI outputs against normative requirements. A multi-agent framework supports evidence extraction, classification, and compliance checking. We introduce Explainable Document Labeling (EDL) to enhance transparency through structured annotations of assessment outputs. Evaluation through industry presentations at the VDA Automotive SYS 2025 Conference, structured demonstrations with automotive suppliers, and systematic reproducibility tests demonstrate that this approach generates standardized outputs with a consistency high enough to work with, addressing one of the major challenges in AI-assisted assessments. Beyond automotive, the findings contribute to understanding how people and intelligent systems can work together effectively in safety-critical industries, illustrating how HCAI principles, intercultural design, and standardized process assessment can jointly advance the reliability, acceptance, and sustainability of intelligent human-systems integration.

Rüdiger Heimgärtner

Conference Proceedings

Exploration of a Generative AI Assistant for Storyboarding and Scenario Generation

Generative Artificial Intelligence (GenAI) has significantly impacted not only creative industries but has found a unique place in military-based applications in storyboarding and scenario generation. GenAI has streamlined this process, making it faster, more accessible, and often more flexible to a larger audience of users. One key advantage is its ability to quickly generate a range of textual and visual concepts from user prompting. GenAI-based storyboards facilitate innovation and collaboration, amongst program managers, engineers, computer scientists, designers and the developers. Use of Large Language Models (LLMs) can be structured to support many features of storyboard creation especially from other perspectives beyond the entertainment business. It supports story drafting, shot list creation, image generation and assembly of the storyboard. This supports communication amongst the stakeholders. This research incorporates an application from initial story creation to the assembly of the storyboard with embedded module functionality found in ComfyUI which is a node-based interface and inference engine for GenAI. This is an open-source tool aid in the execution and integration of features and processes to reach desired outcomes that align with defined goals and objectives of Navy projects.

Bryan Croft, Jeff Clarkson, Tai Nguyen, Seana Rothman

Conference Proceedings

Estimating Product Attributes Relevant to Purchase Decisions from Images in C2C Marketplaces

In recent years, consumer-to-consumer (C2C) online flea markets, which are platforms where individuals buy and sell goods directly, have grown rapidly. Prior studies suggest that consumer behavior on C2C platforms differs from that on business-to-consumer platforms, prompting research that leverages multimodal information, such as images and text. Among these modalities, image analysis plays a key role in revealing visual cues that influence purchase decisions. Manually annotated labels are often used to ensure interpretability; however, large-scale annotation is costly and labor intensive, limiting scalability. This study addresses this issue by developing deep-learning models that automatically estimate the product attributes that affect purchase decisions. We analyzed the product images of tops from a fast-fashion brand posted on a company-operated C2C platform. Using thumbnail images, we built models to predict five visual attributes: (1) Packaged, (2) Folded, (3) Characters, (4) Official Website Image, and (5) Size. Four architectures, namely ResNet, EfficientNet, ConvNeXt, and Swin Transformer, were compared in terms of accuracy. All classification tasks achieved an accuracy of over 90%, with the best-performing model varying by attribute. These results demonstrate that deep-learning-based automatic annotation can effectively reduce labeling costs and support scalable consumer behavior research on C2C platforms.

Kohei Otake, Yoshihisa Shinozawa

Conference Proceedings

Artificial Intelligence-Augmented Social Interfaces: Towards Empathetic and Context-Aware Interaction Systems

The rapid evolution of artificial intelligence (AI) has led to the emergence of socially intelligent systems capable of dynamic, human-centered interaction. However, realizing truly meaningful and empathetic communication remains a key challenge, especially when AI must interpret not only what users say, but also what they mean and feel within a rich social context. This paper proposes a framework for AI-augmented social interfaces (AISI) that combine transformer-based dialogue generation, real-time sentiment analysis, and user profile modeling. The system adapts its tone, engagement level, and responses based on emotional cues, behavioral patterns, and environmental context. A three-phase user study with 60 participants showed that the AISI outperformed a traditional rule-based chatbot across task completion, user trust, and emotional resonance. We also address ethical concerns around transparency, privacy, and explainability, suggesting pathways toward socially responsible AI design. Our findings are relevant to domains such as digital health, education, and collaborative systems where trust and emotional intelligence are essential.

Zhangqi Liu

Conference Proceedings

Robotic Journaling: Translating Raw Robot Logs into Structured Narratives to Model Trust in Heterogeneous Human-Machine Teams

ABSTRACT: Robot-generated logs offer the most comprehensive record of machine behavior and human-machine interactions in field settings, yet their technical complexity renders them inaccessible to human factors researchers. This paper introduces Robotic Journaling, a systematic four-step methodology for transforming technical robot logs into analyzable narratives suitable for rigorous qualitative analysis. The method comprises: (1) systematic log collection, (2) collaborative development of translation codebooks with operators and engineers, (3) transformation of technical logs into plain language narratives, and (4) application of chosen analytical approaches to translated data. We demonstrate this methodology through its application to the DARPA Subterranean Challenge, where NASA JPL’s CoSTAR team operated a heterogeneous fleet of autonomous robots in underground environments. Through Robotic Journaling, we translated 536 pages of fragmented logs from 151 days of field testing into 228 pages of coherent narratives. While we use Grounded Theory analysis of trust dynamics to illustrate how the translated narratives enable qualitative research, this paper focuses specifically on detailing the Robotic Journaling methodology itself rather than presenting analytical findings. This methodology addresses a critical gap in human-machine teaming research by making field-generated data accessible when direct observation is impossible or insufficient particularly vital in high-stakes Real users, Real systems, Real consequences (R3) environments like space exploration, disaster response, and military operations. The method is domain-agnostic and transferable to any research question that could benefit from systematic analysis of robot-generated logs.

Nhut Ho, Benjamin Morrell, Marcel Kaufmann, Boyoung Kim, Nathalie Ochoa, Martin Ha, Elijah Sagaran, Jordan Jannone

Conference Proceedings

Conflict-Free Swarm Shape Formation with Minimal Operator Involvement

Swarms of drones are used in both shows and operational missions (civilian and military). They are complex systems that raise a number of technical issues. Among these is the following. Whatever the context, the members of the swarm must organize according to patterns (or shapes) that depend on the show/mission at hand. To form a given shape, the drones of the swarm must take off and then fly to their respective position of operation (or target position) without colliding with one another. For shows, drones have positions to reach that are known long in advance (i.e. when the show is defined) and their optimal collision free flight path from the ground to this position can consequently be computed off-line, before the show. This can also be supported by the fact that the drones can be organized in an ad hoc on the ground before take-off manner since time permits. For operational missions, it is necessary to act fast and there is no time neither to configure an application that would precompute paths to form a given pattern, nor to organize the drones on the ground to make shape formation easier. Still, if no care is taken, it is most likely that a number of drones will collide with one another (which becomes increasingly likely as swarm size grows). The goal of our work is to address this issue. It should be noted that the possible dynamic behavior of the swarm once the drones have reached their target positions is out of the scope of our current work. Of major importance is addressing the human factor and more precisely the load that is put on the operator. The perfect system should obey the following mode of operation: (i) the operator dispatches the drones on the ground without any position constraint; (ii) each drone is then provided with its current position (that can be acquired by using a GNSS) and its target position. (iii) the operator presses the “start mission” button, and the drones automatically fly to their positions of operation; (iv) the mission is run ; (v) the drones automatically flight back to their original (home) positions; (vi) the operator returns each drone to the storage unit. This closes the mission. In this work, we describe a number of tentative approaches from the literature, others that we have experimented in earlier studies, and those that we are currently developing.

Serge Chaumette

Conference Proceedings

Integration and Testing of a UAS Airspace Management System in the Wildland Firefighting Environment

NASA’s Advanced Capabilities for Emergency Response Operations (ACERO) project explores the use of technology to provide additional aerial support in the wildland firefighting environment by extending the use of Uncrewed Aircraft Systems (UAS) into low-visibility conditions to support sustained operations. A key step in enabling extended UAS operations is the integration of an airspace management system into the wildland fire environment to support the planning, deconfliction, and situation awareness of UAS operations. During Spring 2025, ACERO conducted its first field evaluation with live UAS operations to test the prototype Portable Airspace Management System (PAMS), which allows UAS operators to digitally coordinate multiple UAS operations and share real-time information. PAMS is comprised of an airspace management system, derived from the UAS Traffic Management (UTM) system; an air-to-ground digital communications network; and a graphical user interface (GUI) to support situation awareness. In this paper, we present an overview of ACERO’s first field evaluation, including a description of the PAMS technology, UAS flight operations, and how participants used the GUI to build operational volumes. In the Results section, a summary of questionnaire findings is presented to assess how well the GUI supported situation awareness, usability, and ease of use. We also discuss challenges encountered during field testing and their impact on subjective ratings.

Deborah Bakowski, Lynne Martin, Connie Brasil, Yasmin Arbab, Gregory Costedoat, Stefan Blandin, Charles Walter, Rania Ghatas

Conference Proceedings

Application of Advanced MBSE and System Automation to Improve Home Security System Solutions

This research emphasizes the usefulness of advancements in Model Based Systems Engineering (MBSE) and system automation to enhance the system selection process for a diverse range of stakeholders. The study of this paper, while applicable to any domain, focused specifically on home security systems. As the home security market expands with increasingly complex and varied technological solutions, users often struggle to select a system that precisely meets their needs without being overwhelmed by technical specifications. This paper presents a novel methodology that addresses this challenge by combining the rigorous frameworks of advanced MBSE, requirements modeling, and the capabilities of AI-driven automation. The research details the application of this method to translate user-expressed needs and wants into clearly defined, verifiable requirements. These requirements, which serve as a blueprint of stakeholder desires, are organized into a series of models. This process, using SysML models, automatically generates a curated selection of home security systems tailored to each user. The methodology leverages a Model-Based Architectural Pattern (MBAP) approach to create a Model-Based Pattern Library (MBPL). This library serves as a storage of predefined security system models that encapsulate best practices and standard solutions for common configurations, such as intrusion detection and video surveillance. The process begins with a user assessment to identify needs and wants, followed by requirements and modeling. This information is then used to create a decomposition of the system, breaking down necessary details and components. This systematic decomposition ensures a thorough and detailed analysis of the system's needs. The findings confirm that the integration of stakeholder engagement, requirements writing, and architectural patterns provides a powerful framework for system development. This approach facilitates rapid system customization, improves design quality, and ensures alignment with industry standards. The research successfully modifies the conventional system development lifecycle, proving the utility of a model-based, automated approach in a real-world application. The results highlight a new paradigm for systems engineering, demonstrating how the synergy of MBSE and automation can lead to improved outcomes for both users and developers.

Daijha Hilliard, Bhushan Lohar, John T Wade, Robert Cloutier, Saeed Latif

Conference Proceedings

Examining the Role of Conference Participation to Enhance Research Self-Efficacy and Science Identity of Undergraduates in a Research Training Program

Research training is important for workforce development in Science, Technology, Engineering, and Mathematics (STEM). The STEM higher education literature has identified various components critical to the success of research training programs such as research mentoring and training. Less work has been conducted on other research training activities such as conference participation even though it is an interactive activity that can allow students to develop science self-efficacy and science identity. Yet, the costs associated with conference attendance may be a barrier for students, especially those who already have financial concerns about funding their education. This paper evaluates how conference participation (i.e., conference attendance and/or presentation) contributes to students’ development of research self-efficacy and science identity within a STEM research training program at California State University Long Beach (CSULB) called the Building Infrastructure Leading to Diversity (BUILD) Scholars program. Data were collected from students enrolled in the departments engaged in health-related research across four colleges (natural sciences, engineering, health and human services and liberal arts) at CSULB. This study used propensity score matching and Ordinary Least Squares (OLS) regression to predict research self-efficacy and science identity from exposure to the BUILD intervention and conference participation. Results indicate that both participation in the BUILD Scholars program and conferences have a strong positive association with research self-efficacy and science identity. Overall, the results of this study present a strong case for including conference attendance and presentations as a part of any STEM education research training program

Kim Vu, Hector Ramos, Chi-ah Chun, Panadda Marayong, Jesse Dillon

Conference Proceedings

Preparing Human Factors Graduate Students to Be Human Factors Professionals

The multi-disciplinary nature of human factors requires a range of knowledge and skills concerning academic theories and methodologies to be acquired by students in human factors programs. Universities develop these academic and technical skills and knowledge but is this set of competencies all that is needed for the students’ success as human factors professionals? In the Master of Science Human Factors program at California State University, Long Beach, students are placed in professional settings as human factors interns in paid or non-paid positions, practicing being a professional. Students wrote weekly reflections on the important lessons they are learning in their practicum. The total of 182 essays were subjected to a qualitative analysis to identify the emergent concepts which were essential for their success in their professional placements. Eleven (11) concepts of the skills and knowledge emerged from their essays with ten (10) related to project management in dynamic situations and career planning and growth. Only one was focused specific to human factors methods. Experiencing the changing and unexpected demands within the workplace enables students to practice their problem-solving and project management skills as well as developing resilience in maintaining a positive outlook within dynamic and often challenging circumstances. Providing regular opportunities for students to reflect on their professional employment skills helps them recognize and prepare for the challenges they will face in their early careers.

Gerry Hanley, Marielle Hanley

Conference Proceedings

Design and Evaluation of Message Thinking based on Futurability Education

Human decision-making is often shaped by “temporal myopia” and “present bias,” which lead individuals to undervalue the significance of their future selves and others, resulting in a tendency toward short-term choices. In today’s world, where life expectancy is increasing and career paths are becoming more diverse, it is increasingly important to counteract these tendencies by cultivating decision-making abilities based on long-term and multidimensional perspectives. To address this issue, Saijo proposed the concept of “Future Design,” which aims to build a society that incorporates the interests of future generations. At the core of this framework is the concept of “Futurability,” which refers to the idea that even when present benefits are sacrificed, decisions and actions that contribute to the well-being of future generations can enhance human happiness. Expanding on this concept, Kurashiki introduced “Futurability Education,” an educational intervention framework designed to activate individuals’ inherent potential for Futurability. The framework consists of three phases: (1) recognition of Futurability, (2) cognitive training, and (3) exploration of trade-offs, all aimed at strengthening decision-making capacity. In this study, we developed and tested a practical method of Futurability Education called “Message Thinking.” In this approach, participants take on various temporal and generational roles and write messages to their future selves from each role. The exercises were designed to promote perspective shifts and foster long-term decision-making skills, and their educational effects were empirically evaluated. The study focused on two roles: the “present parent” and the “future parent.” We examined psychological changes before and after the exercise, with particular attention to how participants acquired a future-oriented perspective. The intervention was conducted with 95 graduate students from the Graduate School of Engineering at The University of Osaka. Participants engaged in the Message Thinking exercise and completed pre- and post-surveys. The surveys included the Career Resilience Scale, the Career Decision Self-Efficacy Scale, and items assessing awareness and willingness related to prosocial helping behaviors. Results showed significant improvement in four subscales of the Career Resilience Scale: “ability to cope with problems and change,” “social skills,” “optimism about the future,” and “willingness to help others.” Improvements were also observed in two subscales of the Career Decision Self-Efficacy Scale: “goal selection” and “degree of independence in decision making.” Furthermore, four out of eight items related to prosocial awareness and willingness demonstrated significant gains, especially those addressing support for future others. All of these improvements were confirmed using the Wilcoxon signed-rank test. These findings suggest that Message Thinking enabled participants to reflect on their behavior and decision-making from perspectives that are rarely accessed in daily life, such as those of present and future parents. This experience appears to have supported psychological shifts in career-related thinking and increased their awareness and willingness to support others. In particular, the observed enhancement of future orientation and prosocial attitudes toward future others indicates that Message Thinking holds promise as an effective educational intervention for activating Futurability.

Taisei Naganawa, Kazuhito Wakamoto, Tetsusei Kurashiki

Conference Proceedings

Usability Heuristics for Costume Design and Quality Ergonomics in the Entertainment Industry

The entertainment business continues to push the limits for human performance and technologies by augmenting performances with costume technologies. A major challenge in the design and applications of costumes is the lack of ergonomic standards to ensure successful performance while minimizing injuries. This paper will present an application of Nielsen’s usability heuristics to create heuristics for costume design with the usability principles for user interface design reimagined and applied to costume design. Fourteen usability heuristics make up the framework for the Costume-Apparatus Usability Heuristics (CAUH), with two strategies for applying the CAUH. One strategy evaluates the usability of each costume element separately, which is useful because the costume-apparatus elements are frequently designed and produced by different people. The second strategy is a more holistic evaluation of the configuration of costume elements and their impact across the full lifecycle of the performance. The implications for the adoption of the CAUH within the entertainment industries can produce multiple benefits, from reducing injuries to enabling visionary performances.

Brandon Takahashi, Gerry Hanley, Marielle Hanley

Conference Proceedings

Non-invasive transcutaneous vagal nerve stimulation enhances mood, task performance, and learning in a high-stress military training environment

Non-invasive transcutaneous vagal nerve stimulation (tVNS) has been shown to accelerate learning and performance in Air Force (USAF) personnel while simultaneously increasing attention, arousal, and mood in well-controlled laboratory tasks. The purpose of this study was to evaluate the effect of tVNS on operational performance, cognitive function, and mood in Air Force trainees undergoing the third week of their Military Qualification Training (MQT) course, the most difficult and fast-paced portion of their curriculum. Methods: USAF trainees were randomly assigned to receive active tVNS or a sham device. On days 1 through 4, trainees completed a 15-item mood questionnaire (pre-task) followed by tVNS or sham, MQT tasks, another round of tVNS or sham, and a final mood questionnaire (post-task). They completed a third mood questionnaire at the end of each day (EOD). Stimulation consisted of 2 minutes of tVNS or sham on one side of the neck followed by a 2-minute break and then 2 more minutes of tVNS or sham on the other side of the neck. Mood was measured using a 7-point numeric rating scale. A mixed-model ANOVA, with “group” as a between-subjects factor and “day” as a within-subjects factor, was used to assess mood changes from pre-task Day 1 ratings to all post-task ratings for each day and pre-task Day 1 ratings to all EOD ratings, as well as changes in task performance. Results: A total of 70 trainees completed the study (39 active, 31 sham). Trainees who received tVNS reported reduced distress (Distressed/Delighted, p<0.001), increased ability (Able/Unable, p=0.020), increased energy (Fatigued/Energized, p=0.002), and an improvement in overall mood (p=0.026) from pre-task Day 1 to all post-task ratings, compared to the sham group. Increased energy (p=0.034) and reduced distress (p=0.042) were maintained at EOD for those receiving tVNS compared with sham. There was a trend toward increased focus (Focused/Distracted, p=0.064) from pre-task Day 1 to all post-task ratings for tVNS vs sham. For performance of MQT tasks, trainees receiving tVNS demonstrated a significant increase in the ability to produce full motion video (FMV)-derived intelligence products in support of mission tasking compared to sham (p=0.020). A significant effect of tVNS compared with sham was observed on Day 1 for the “perform FMV callouts using the appropriate format” and “conduct a target walk-on target verification” tasks. tVNS produced additional performance benefits that did not reach statistical significance. Conclusions: This is the first demonstration of tVNS in a high-stress, high-performance real-world operational training environment. Trainees receiving tVNS saw improvements in several measures of operational readiness, were significantly more energetic, less stressed, and felt better able to perform the required tasks. These operational and mood related improvements in FMV-related tasks suggest that tVNS could be deployed to enhance warfighter training and operational readiness.

Lindsey McIntire, Patrick O'maille, Eric Liebler, R. Andy McKinley

Conference Proceedings

Exploratory Study on Visualizing Multilayered Psychophysiological Change in Creative Activity

This study aims to visualize the multilayered psychological change processes that occur during creative activities, with a particular focus on their role as learning processes. Creative Self-Efficacy (CSE) was adopted as the primary indicator, while optimism and General Self-Efficacy (GSE) were measured as complementary constructs. This approach allows creativity, as an abstract concept, to be empirically captured, and examines how creative engagement functions as a developmental learning experience. Creative workshops using LEGO® Serious Play® (LSP) were conducted, and changes in psychological indicators before and after the sessions were analyzed. In addition, electroencephalographic (EEG) data were recorded during the sessions to trace cognitive and emotional processes, thereby clarifying the dynamics of learning through creativity.The PERMA model from positive psychology (Positive emotion, Engagement, Relationships, Meaning, Accomplishment) was positioned as a supplementary framework to explain part of the observed changes. The central focus, however, lies in the visualization of psychological transformations occurring during creative learning activities, with PERMA serving as a theoretical lens to interpret specific outcomes. Ultimately, cultural factors such as optimism, personality traits (e.g., Big Five), and value orientations are also considered to clarify how creative activity, as a form of experiential learning, contributes to psychological efficacy and well-being.As a preliminary study, a literature review was conducted to organize the theoretical relationships among optimism, CSE, and PERMA, followed by qualitative discourse analysis of Japanese in-house designers. The analysis revealed culturally embedded components of optimism. “Bright outlook” was most frequently mentioned, followed by “absence of anxiety,” “flexibility,” “confidence,” and “carefree orientation.” These findings were generally consistent with the established five-factor structure (Uochi et al., 2020) but also revealed overlaps among factors, suggesting a multifaceted cultural understanding of optimism in creative activity. Based on these insights, a hypothetical model was constructed to guide further empirical testing.In the subsequent phase, individual participants from private companies and educational institutions took part in LSP workshops, which are designed to promote divergent thinking, collaborative problem-solving, and metaphorical expression. EEG data were recorded during the workshops (creative activities) to capture neural patterns associated with engagement and learning. Pre- and post-intervention measures included optimism (LOT-R and culturally adapted items), CSE, and GSE. Changes were analyzed using paired t-tests and hierarchical multiple regression analysis, while factor analysis of the optimism scale was conducted to extract culture-specific (Japanese) structures.A distinctive feature of this study is its focus on individual-level creative interventions, rather than group-based approaches. This design minimizes social facilitation and conformity effects, enabling clearer examination of the mechanisms through which creative learning enhances CSE (self-confidence), GSE (sense of growth), and PERMA (well-being) dimensions. In particular, it may clarify pathways that are independent of interpersonal feedback, such as from Positive emotion (P) to CSE, and from GSE to Engagement (E) and Accomplishment (A). Furthermore, by integrating Japanese-specific factors of optimism into the PERMA framework, this study proposes a culturally adaptive model of creative self-efficacy development as a learning outcome.

Mika Isobe, Yoshikuni Edagawa

Conference Proceedings

Designing Music Training Systems for Deaf and Hard-of-Hearing Individuals: Insights from Multi-Element Perception Tasks

Many Deaf and Hard-of-Hearing (DHH) individuals enjoy listening to music and singing. However, the way music sounds to them varies from person to person, and even among those with the same level of hearing loss, perceptions differ. Furthermore, DHH individuals focus on different elements when listening to music. Although music training systems for DHH individuals have existed before, many were designed and implemented in laboratory settings. We aimed to create a music training system for DHH individuals that could be used playfully in real-world environments, such as on their own smartphones or tablets, and for researchers and educators to provide an easily extensible framework. This presentation describes the versatile framework and trial experiments with DHH participants. The results revealed the importance of selecting training materials and setting training difficulty levels, especially for DHH participants whose music perception is affected by several factors, including hearing levels, interest in musical elements, and musical experiences. We envisioned a music training system integrating subsystems designed to help DHH individuals hear more of what they want to hear when listening to music. The subsystems share the same user interface, presenting the target audio and four alternative audio samples. Separate instructions indicate which selection to make, without visual or haptic cues. As users must listen carefully to the target sound and compare it with the alternatives, we call this subsystem “Music Memory.” We created Music Memory for four musical elements using this simple interface. We set problems for each of these: 1) selective listening, 2) instrument identification, 3) recognizing melody or rhythm variations, and 4) tempo recognition. To investigate whether the difficulty levels set for each of the four Music Memories were appropriate and whether the acoustic data for the alternatives presented matched the target audio, experiments were conducted with DHH participants. The problem audio sources were selected from J-POP and anime songs, which DHH frequently listens to. Between 8 and 20 young DHH participants took part in the four experiments. The DHH participants in the four Music Memory experiments differed (with some overlap).The accuracy rates for the four memory experiments were: 1) mostly ceiling effect observed, 2) ceiling effect observed, 3) slightly above 50%, and 4) slightly below 25%, namely below the chance level. For condition 3, a tendency was observed where higher musical experience correlated with higher accuracy rates. The accuracy rates, though averages, indicate the problem settings that are either too easy or too difficult. This suppresses the user's motivation to continue training and makes it difficult for trainers to refine the provided materials. For use in training, the slightly above 50% accuracy rate observed in 3) might be appropriate among the four Music Memories. However, in 3), concerns were raised about the lack of criteria for creating alternatives involving melody or rhythm alterations, potentially leading to subjective provision of acoustic data.These findings highlight the necessity of carefully designing training difficulty levels and instructional materials before applying the system in practice. Not only for 3), but all three other Music Memories need the objective provision to provide alternatives. For trainees to keep using Music Memory, the interface will need to give feedback on their answers, and we may consider additional sensory modalities to reduce the burden of using Music Memory for DHH people. This research has established a foundation for the practical implementation of a music training system for DHH individuals. However, future challenges include refining the personalized adaptation algorithm, verifying long-term training effects, and conducting evaluations with a larger participant sample. Then the system will contribute to the realization of music training environments that are optimized for each individual DHH and to construct inclusive music education environments. This research indicates a new research direction in the interdisciplinary field of assistive technology for the DHH people and music education.

Rumi Hiraga, Hiroko Terasawa

Conference Proceedings

Elementary School Students’ Preferences and Learning Effects on Displayed Teacher Image in On-Demand Learning Content

This study aims to elucidate the appropriate form of teacher images in online learning content and to establish the knowledge necessary to construct effective learning content. Based on our previous study, we hypothesized that the improvement in learning effect would be maintained or continued when the preference for teacher image presentation matched the learners’ preferences for the display in the learning content. To confirm the validity of this trend, we experimented. We also analyzed the difference in learners’ gazing points when their preferences were matched and when they were not. In this paper, 41 elementary school students (grades 4-6) were given a quiz simulating on-demand learning, and the quiz’s correct response rate and gazing point were analyzed. Questionnaires and interviews were also conducted. Two types of content were created and used: one with the teacher’s image and the other without it. The content viewed by the participants included an explanation of the correct answers. The learning retention rate was analyzed based on the percentage of correct answers on the quiz before and after viewing this explanation, and then again after four weeks. In the analysis, we tested for differences in learning effects and gazing areas by whether the teacher image was presented and if it matched the participants’ preferences for the presentation of the teacher images. The results of the interview revealed that 46% of the experimental participants preferred the presentation of the teacher’s face image. It was also found that the presence or absence of the teacher’s image in the content did not affect the percentage of correct answers to the quiz. However, when the presence or absence of teacher images in the video content viewed matched the individual preferences of the participants, the learning effect was improved up to four weeks after the experiment. Furthermore, we compared the gazing areas of the groups in which the presence or absence of the teacher image matched the students’ preferences with those in which they did not. The results showed that the group who preferred the teacher image, in other words, the group whose preference matched the teacher presentation, spent significantly less time looking at the teacher image in the learning content than the group who didn’t prefer the teacher image, and spent considerably more time looking at the area of multiple choices during the explanation period. These results suggest that among participants who prefer the presentation of teacher image, there is less concern that gazing at the teacher image will reduce their attention to the learning content. The interview results indicated that the presentation of teacher images can be a reassuring or disincentive factor, depending on learner preference. To construct learning content with a high learning effect, it is essential to design a system that encourages learners to think and understand spontaneously, considering their preferences rather than just the presence or absence of teacher images.

Satori Hachisuka, Kayoko Kurita, Shinichi Warisawa

Conference Proceedings

Visual-Cognitive Profiling Using Eye Movements and Brain Activity: Validation of a Novel Assessment Tool

In recent years, a growing number of individuals in educational and workplace settings have experienced non-pathological difficulties, such as fluctuating work efficiency and diminished self-efficacy stemming from an inability to identify learning or working methods that suit their personal cognitive styles. Although these challenges often fall outside the scope of medical or welfare-based support systems, they nonetheless have a substantial impact on daily functioning and performance. Moreover, the persistent cultural notion that “mental care is only for those with illnesses” creates a psychological barrier to seeking help, thereby widening the “support gap” in society. Conventional support systems tend to prioritize individuals with clinical diagnoses, offering limited opportunities for proactive and personalized approaches that empower individuals to optimize performance through self-understanding and cognitive enhancement. To enable such approaches, objective indicators of individual characteristics are essential. Visual-related cognitive functions—such as eye movements, visual field, spatial cognition, and working memory—are known to correlate with learning outcomes, work efficiency, and stress regulation, and are considered trainable. However, no existing framework integrates cognitive assessment, physiological measurement, personalized feedback, and targeted training into a single, practical system. To address this need, we developed and evaluated a tablet-based application, Diabi Eye. This tool assesses user tendencies across four cognitive domains—reading comprehension, memory retention, spatial cognition, and attentional control—by analyzing visual processing and eye movement patterns. Its accuracy was validated through both eye-tracking data and cerebral blood flow measurements. Participants with high reading comprehension scores demonstrated a positive correlation between Diabi Eye performance and activation in the dorsolateral prefrontal cortex (DLPFC), a brain region associated with working memory. In contrast, participants with low spatial cognition scores exhibited a negative correlation in the same region, suggesting distinct neural activation patterns aligned with specific cognitive strengths and weaknesses. Additionally, gaze pattern analysis revealed characteristic eye movement behaviors corresponding to different cognitive profiles, indicating that specific visual behaviors reflect underlying cognitive tendencies. These findings also suggest the potential for improving cognitive weaknesses through targeted visual-cognitive training. Finally, we evaluated pre- and post-intervention outcomes using the app’s built-in training module. Results demonstrated the system’s potential to support the self-optimization of learning and working strategies in everyday life. This study underscores the utility of Diabi Eye as a novel, integrative tool for early, non-clinical cognitive support, bridging gaps between health, education, and occupational performance.

Masami Matsushima, Tomofumi Sakata, Syunpei Kiuchi, Keiichi Watanuki

Conference Proceedings

MECHA: Modular Equipment Chat Helper Agent for Maintenance and Operation of Machinery Used in Heavy Equipment Production Lines

Currently, large language models have introduced numerous new ideas for further automation in the industrial sector. The application of large language models primarily focuses on three areas: knowledge bases, workflows, and intelligent agents. For instance, the manufacturing industry has started using knowledge bases to manage the vast amount of documents generated during research and development and production processes, enabling engineers and workers to retrieve knowledge more quickly.However, due to differences in the proficiency of on-site maintenance personnel, user retrieval habits, and the limitations of information available on-site, directly constructing a knowledge base for queries cannot provide truly practical maintenance operation suggestions for on-site personnel. This study, based on the large model knowledge base and multi-agent technology, constructs an intelligent agent system for production line operation and maintenance in industrial production processes, offering applications for Q&A and multi-agent multi-turn Q&A fault diagnosis.

Jiale Wang, Le Ling, Wenliang Wu, Ruiqi Lin

Conference Proceedings

Impact of Retrospective Confidence Prompts on Students’ Metacognitive Awareness and Skills

The capacity to comprehend and control one's mental processes, such as observation, assessment, and problem-solving, is known as metacognitive awareness. This research focused on a group of undergraduate students taking a foundational programming course during the semester. The aim was to explore how retrospective confident judgment questions influence students' metacognitive awareness—a crucial skill in self-regulated learning. To assess their metacognitive awareness, we employed the Metacognitive Awareness Inventory (MAI), a tool designed to illuminate the nuances of their cognitive processes and self-reflection (Schraw & Sperling, 1994). This is a validated self-report tool that measures two fundamental dimensions—knowledge of cognition and regulation of cognition, as well as their subcategories—and was used to assess metacognitive awareness. This research involved administering the MAI at two distinct points in time: the initial assessments took place in January and February, which we designated as the "beginning" phase, while the follow-up evaluations occurred in April and May, referred to as the "end" phase.

Michael-brian Ogawa, Sara Mostowfi, Jung Hyup Kim, Kristen Shinohara, Danielle Oprean, Curtis Ikehara, Yuanyuan Gu, Leah Ten Eyck, Martha Crosby

Conference Proceedings

Pilot Training Modalities in Aviation: A Systematic Review of Their Impact on Safety

Aviation safety remains a global concern, with human error a leading cause of incidents and accidents. This systematic review synthesizes findings from 22 studies (2015–2024) on the impact of pilot training, organized into six themes: simulation-based training, scenario- and task-based training, organizational and academic programs, checklist-, workshop-, and paper-based methods, safety culture and fatigue management, and cognitive adaptation to technology. Simulation and scenario-based training improved situational awareness, hazard recognition, and decision-making under stress. Organizational and academic programs, particularly those embedded in Safety Management Systems, strengthened safety climate and proactive risk management. Checklist and workshop approaches effectively addressed hazardous attitudes and reinforced procedural discipline, while safety culture and fatigue management interventions targeted systemic risks and resilience. Training for cognitive adaptation supported older pilots in transitioning to advanced cockpit technologies. Most studies focused on commercial aviation, with limited evidence from other sectors and few evaluations of long-term outcomes. Future research should integrate these modalities and assess their sustained impact on safety performance.

Peter Szawranskyj, Avishek Choudhury

Conference Proceedings

Gamifying Instructional Videos Did Not Lead to Better Student Comprehension

Online learning has become a widely adopted method of education. Although it offers many benefits, it also comes with certain drawbacks. For instance, asynchronous courses provide students with flexibility, but distractions in the study environment can affect students' ability to concentrate when viewing online lectures. Moreover, students may not be motivated to continue to watch if the instructional videos are not engaging. To potentially counter this latter issue, the present study examined whether gamification could benefit online learning by improving students’ comprehension of video lecture content. A 2 (Gamification Level: Gamification or No Gamification) x 2 (Content Difficulty: Easy or Hard) mixed design was used. Participants were assigned to either a gamification condition or a non-gamification condition. For both conditions they watched instructional videos containing easy- and hard-level content and were tested on their comprehension of the video content. It was hypothesized that gamification would keep students engaged with the instructional videos, leading to higher scores on comprehension quizzes. However, results of the present study showed no effect lecture video gamification on the quiz scores. Implications of these findings for online learning are discussed.

Nicole Pham, Kim Vu

Conference Proceedings

Motivating Information Explorers: AI-based Orientation System for Promoting Web-based Investigative Learning

Web-based investigative learning is an example of information exploration. Learners are expected to explore learning resources according to their interest in order to construct wider and deeper knowledge regarding their question. In our previous studies, we have modeled a process of investigative learning as a cycle of three phases: (1) searching and navigating Web resources, (2) knowledge construction, and (3) question expansion, and supported learners’ metacognitive activities with a cognitive tool we have developed. However, learners are required to be motivated for learning in order to engage in such self-regulated learning. In this paper, we propose an approach to promote learners’ motivation by providing information regarding their initial question so that they can perceive value in their investigation. To provide orientation according to learners’ values, we have classified the value types and viewpoints to present information about the question based on their values. We also propose a system that provides the orientation by generating summaries about their question using a large language model (LLM). According to a preliminary case study, it is suggested that the orientation approach promotes learners’ knowledge construction and deep question expansion.

Yutaka Watanabe, Akihiro Kashihara

Conference Proceedings

The Influence of Future Temporal Distance on Decision-Making in Futurability Education

This study examined how introducing perspectives from different future time points influenced high school students’ awareness of social issues and their prioritization of policy measures. "Future Design" has been proposed in recent years to address long-term social issues such as environmental problems. A person exhibits “futurability” when his or her experiences an increase in happiness as a result of deciding and acting to forego current gains in order to enrich future generations, and “futurability” is an important in achieving a sustainable society. Future Design involves practical applications such as municipal policymaking, aimed at achieving a sustainable society. Kurashiki, one of the authors, proposes “futurability education” to foster futurability. One of the effective methods to activate futurability is the use of "Imaginary Future Generations" (IFGs), which represent stakeholders acting on behalf of future generations. In previous studies on futurability education, a generation has been defined as 20 years, and IFGs have typically been set approximately 40 years ahead. However, little research has examined how the setting of future time point for IFGs affects thinking and decision-making, despite its importance in both futurability education and Future Design. This study researches this gap by comparing IFGs set at three different time points: 20 years (2043), 40 years (2063), and 60 years (2083) into the future. The social experiment involved 78 high school students who participated in a group workshop during a university open campus event in August 2023. Students worked in groups of four and were presented with policy cards based on Ikeda City’s 6th Comprehensive Plan in Osaka Prefecture, Japan. By analyzing which policy cards were selected, the study explored how decision-making changed across timeframes. The results showed that revitalizing rural areas and promoting labor policy were more likely to be prioritized when the imagined future was more distant. In contrast, the policy like disaster preparedness were favored for the nearer future. These preferences seem to reflect anticipated societal issues in Japan, such as urban overpopulation and the replacement of labor through AI technologies. On the other hand, the relatively low selection of disaster-related policies for the distant future suggests that students assumed major events, like the predicted Nankai Trough mega-quake, Japan, would have already occurred by then. Across all future generations, high selection rates were observed for policies promoting international exchange, countermeasures against declining birthrates and child-rearing support. This indicates a strong awareness of globalization and Japan’s pressing demographic issues. Meanwhile, infrastructure-related policies such as those involving waterworks or road networks, were selected less often, likely due to the perception that Japan’s infrastructure is already well-developed. This study revealed that changing the time period set for IFGs leads to differences in policy preferences and decision-making tendencies. The findings suggest that, in future-oriented education, setting an appropriate future time point according to the topic and educational objectives can enhance the overall educational effectiveness.

Katsutoshi Michihata, Shohei Nakamura, Tetsusei Kurashiki, Kazuhito Wakamoto

Conference Proceedings

A Qualitative Study on the Factors and Mechanisms Impacting STEM+C Undergraduates’ Enrollment and Persistence

Over the last few decades, the graduation rates for post-secondary education in science, technology, engineering, mathematics, and computing (STEM+C) have stayed consistently low despite efforts to increase participation in STEM+C disciplines. Prior teaching and educational research have primarily focused on one specific degree pathway within STEM+C, such as computer science. However, understanding the common factors and mechanisms that influence STEM+C students to persist in their different degree programs has been left largely unexplored. In this study, the research team investigates the factors that influence the enrollment and persistence of undergraduate STEM+C majors at a large, public Hispanic-Serving Institution (HSI) in Texas. As part of a larger mixed-methods investigation, data were collected from a total of 168 undergraduate STEM+C majors using the ACCEYSS STEM+C survey instrument to evaluate factors impacting their enrollment and persistence in their degree programs. Qualitative analysis was performed on the survey responses from 65 science majors, 45 computing majors, 36 engineering majors, and two mathematics majors. Participants were asked about their career aspirations, the types of learning experiences they participated in prior to college, and the factors that influenced them to pursue their major. Also, participants were asked to describe what students needed to be successful in their chosen field and what advice they believe would help incoming students persist to graduation. The findings of this study revealed that students across all majors: (a) indicated their decisions to pursue STEM+C majors were mainly influenced by personal aspirations and motivation, followed by self-confidence, self-efficacy, and perceived intelligence; (b) offered advice for incoming STEM+C majors focused on building resilience, effectively utilizing resources, developing strong learning strategies, and maintaining motivation throughout their academic journey; and (c) identified key factors for success in STEM+C degree programs such as establishing efficient time management skills, cultivating good study habits, and engaging in self-directed learning with an emphasis on continuous skill development and problem-solving approaches. The results of this study provide key insights and recommendations to help guide post-secondary educators’ and policymakers’ decision-making to cultivate a university environment that actively supports more students to persist in STEM+C degree programs and reach graduation.

Kusum Bhattarai Sharma, Ila Wallace, Shreya Upreti, Ruchi Kukde, Shetay Ashford Hanserd

Conference Proceedings

CODEM: A Microworld Platform for Research, Training, and Assessment in Complex Problem-Solving

Complex problem-solving (CPS) skills – the ability to comprehend, manage, and adapt to complex, evolving situations – is essential in the 21st-century workplace. However, empirical evidence shows individuals are limited, computationally and cognitively, in managing complex systems (e.g., delayed feedback, nonlinearity, and conflicting goals). Traditional cognitive tasks are deemed too simple and often fail to capture these properties, whereas field studies lack control over conditions and measurement. Microworlds offer controlled complexity: they can reproduce properties of complex systems but remain tractable for systematic manipulation and data collection. We wish to present and demonstrate CODEM (COmplex DEcision Making), a microworld platform designed to simulate complex dynamic systems at different levels and trace the cognitive processes underlying CPS and decision-making. CODEM serves as a testbed where one can build environments with customizable variables, feedback loops, semantics, and opacity. The platform provides performance (e.g., comparing human goal attainment with random simulations), cognitive process-tracing (e.g., use of heuristics) and behavioural (e.g., structural information seeking) logs, intelligent tutor extensions (e.g., for system thinking training), and multiplayer options for collaborative problem solving. CODEM has three key applications. First, as a research tool, it enables systematic study of CPS and decision heuristics under complexity. Second, as a training and awareness tool, it highlights pitfalls in reasoning (e.g., most individuals assume linearity and neglect delayed effects) while promoting system thinking and metacognitive strategies. Third, as a personnel selection tool, it holds promise as a measure of the capacity to manage complexity beyond general intelligence. Results from a series of experiments showed that participants perform poorly on CODEM complex scenarios, often close to or even below chance levels, despite the presence of system thinking behaviour. These results are in line with the view that complexity poses a dire cognitive challenge, highlighting the need for tools to assess, train, and support CPS.

Sébastien Tremblay, Delphine De Hemptinne, Gabrielle Teyssier Roberge, Benoit Béchard, Daniel Lafond, Alexandre Marois

Conference Proceedings

Introducing the CARES Model: Integrating Artificial Intelligence, Medical Education, and Patient-Centered Care

Artificial Intelligence (AI) is transforming the delivery of patient-centred healthcare in Canada and around the globe. As AI becomes mainstream in daily clinical practice, it is increasingly critical to equip physicians and medical trainees with the skills to effectively integrate AI into patient-centered care. In Canada, medical education is guided by the CanMEDS framework, which is structured around seven CanMEDS roles: Medical Expert, Communicator, Collaborator, Leader, Health Advocate, Scholar, and Professional. Despite the growing influence of AI in healthcare, there is a notable absence of AI-specific competencies within medical education for critically evaluating AI tools, interpreting AI-generated outputs, and safely and ethically integrating AI into clinical decision-making. To bridge this gap, we suggest a new model for physicians and medical trainees to critically evaluate the use of AI in clinical practice, based on patient-centered principles. This model is based on the core concepts of Communication, Autonomy, Respect, Equity, and Safety, which together form the CARES model. Integrating the CARES model into medical education should adopt a constructivist approach, leveraging active learning, case-based scenarios, simulations, and real-world experiences to prepare learners for the complexities of AI in clinical practice. Our research suggests that the CanMEDS framework offers an ideal foundation to explore the core domains of the CARES model, which can be adopted and integrated into daily clinical practice to promote digital literacy. Importantly, the CARES model can be adapted to fit existing medical curricula and tailored to align with global efforts to integrate AI into medical education. Additionally, we have found that central to this approach is the incorporation of feedback loops from both learners and instructors to ensure a sustained focus on patient-centered care. Our findings highlight the opportunities presented by the CARES model to promote digital literacy among physicians and medical trainees in a novel way using the existing CanMEDS framework. By leveraging the flexibility of the CanMEDS framework, we hope to increase digital literacy among physicians and medical trainees. The CARES model represents a novel approach to prepare the next generation of healthcare providers to use AI safely and effectively in their practice while maintaining a patient-centered focus.

Bryan Johnston, Jay Kalra

Conference Proceedings

The Impact of Pharmacist Expertise on Information Gathering During Prescription and Medication Verification: Eye-Tracking in a Simulated Experiment

In Japan, pharmacists perform "prescription verification" to assess the medical and pharmaceutical validity of prescriptions and "medication verification" to ensure that dispensed medications match the prescriptions. These processes are critical for patient safety. Experienced pharmacists are generally more accurate and efficient than novices, but their expertise remains largely tacit and unshared. This study aimed to compare eye-gaze patterns between experienced and novice pharmacists in a simulated verification task and to clarify how expertise influences information-gathering behaviors.[Methods] In the simulated verification experiment, 22 fictional prescriptions (each containing three medications) and 66 corresponding medication packages were prepared. Eleven of these prescriptions included typical dispensing errors based on actual incidents at our hospital. Eight pharmacists participated (novices: five with 1–2 years of experience; experts: three with over 20 years of experience) and performed the medication verification as they would in routine practice. Eye-tracking data were collected using Tobii Pro Glasses 2 (100 Hz). Average fixation duration and fixation count were calculated using Tobii Pro Lab. The statistical analysis of these data was conducted using the Mann–Whitney U test to compare the two groups of experts and novices (Matlab R2024b). Additionally, Heatmaps and Gaze plots were generated, and these data were input into Google NotebookLM to analyze the differences in gaze patterns between the two groups.[Results] The statistical analysis showed that experts had significantly longer average fixation durations than novices (experts: 263.0 ± 19.8 ms; novices: 182.3 ± 16.8 ms; U = 6.00, z = –0.45, p = .036). However, no significant difference was observed in total fixation counts (experts: 4333.3 ± 380.7; novices: 5708.2 ± 913.8; U = 8.00, z = –4.62, p = .071). Heatmaps indicated that experts broadly scanned patient names and multiple prescription items, whereas novices concentrated on limited information such as medication names and dosage instructions. Gaze plots revealed that experts tended to have longer saccades, rapidly shifting between distinct information blocks, while novices displayed shorter saccades with repetitive focus on limited areas.[Discussion] Although the total number of fixations was not significantly different between novices and experts, novices exhibited significantly shorter average fixation durations. In addition, the fixation areas for novices were limited to critical information areas on the prescription, and they frequently moved back and forth between limited information blocks. This suggests that novices may frequently recheck minimum required information such as medication names and dosage instructions in a short time. On the other hand, experts had longer average fixation durations and may have been deeply concentrating on the target, reading the information thoroughly in a single fixation, suggesting deeper cognitive processing during each fixation. Experts also showed broader visual coverage of the prescription, potentially enhancing verification accuracy through comprehensive checking. These findings may contribute to the development of eye-tracking-based expertise assessment, feedback-driven training programs, and improved prescription interface designs for pharmacists.

Yoshitaka Maeda, Kosuke Oiwa, Masahiro Katano, Mariko Tsurumi

Conference Proceedings

Interoperable medical device GUI for SDC Workstations

This paper investigates whether, within the framework of interconnected medical devices, a graphical user interface (GUI) that is developed based on standardized, machine-readable user-interface requirements (UI Profiles) can achieve the same level of usability and safety as commercially available High Frequency (HF) devices. The results of this study demonstrate that a GUI generated from a standardized UI Profile can be as safe and usable as established solutions on the market. The UI Profile-based GUI and two commercially available solutions (BOWA ARC 400, ERBE VIO® 3) were evaluated in a formative usability study by 17 clinicians at the University Hospital Aachen and University Hospital Essen, Germany. The task completion times and rates, user satisfaction, and learnability have been measured and evaluated. The UI Profile-based GUI performed well for all tasks and achieved similar performance levels compared to the established solutions. UI Profiles could provide a practical complement to medical device interoperability standards (IEEE 11073 SDC), enabling the exchange of usability-related data and commonly agreed device (type) specific human-machine-interface requirements.

Okan Yilmaz, Miriam Lange, Klaus Radermacher, Frank Beger, Sven Kämmer, Thomas Maser, Peter Selig, Björn Seitz, Simon Kißmann, Armin Janß

Conference Proceedings

Assistive Exoskeleton Technologies for Age-Related Mobility Impairments

The global demographic shift toward an aging population has intensified the demand for innovative solutions to support elderly individuals in maintaining mobility and independence. Among emerging technologies, exoskeletons—wearable robotic devices that augment human movement—show great promise in this area. This literature review synthesizes recent research on the use of exoskeletons to assist elderly individuals with mobility impairment. Studies indicate exoskeletons significantly enhance mobility, balance, and gait performance in older adults, particularly those affected by conditions such as stroke, Parkinson’s disease, and osteoarthritis. Technological advancements, including the integration of lightweight materials, improved actuator systems, and adaptive control algorithms, have contributed to the usability and effectiveness of these devices. Moreover, the incorporation of biomechanical modeling and Internet of Things (IoT) connectivity has enabled personalized and real-time feedback mechanisms, further enhancing user experience. Despite these advancements, challenges remain in terms of device affordability, accessibility, and long-term adherence. Usability studies emphasize the importance of intuitive interfaces, aesthetic design, and minimal physical strain to encourage adoption among elderly users. Clinical trials and case studies demonstrate positive outcomes, yet limitations such as small sample sizes, short intervention durations, and lack of standardized evaluation metrics hinder the generalizability of findings. This review also highlights the growing trend of open-source exoskeleton platforms, which foster collaborative development and customization. In conclusion, while exoskeletons hold substantial potential to improve the quality of life for elderly individuals, further research is needed to address existing limitations and ensure equitable access. Future directions include the development of cost-effective models, longitudinal studies to assess sustained benefits, and policy frameworks to support integration into healthcare systems.

Sohyung Cho, Greg Wiles

Conference Proceedings

Nurses scheduling under epidemic crises: balancing demand coverage and nurse fatigue

During epidemic crisis (EC) situations, meeting urgent medical demands is crucial. However, medical staff, especially nurses, often experience more fatigue due to sharply increasing workloads and irregular shifts during EC. The sustained pandemics exacerbate the fatigue issue and further cause higher turnover and understaffing of nurses, which may jeopardize the whole healthcare system. It is essential to adopt a prudent scheduling method that can balance urgent demands and nurse fatigue during epidemic crises. This paper aims to provide a solution considering the balance of nurse fatigue and the satisfaction of demands for a nurse scheduling problem during epidemic crises. We used the bio-mathematical model of fatigue (BMMF) to predict the fatigue of nurses and constructed a mixed-integer nonlinear programming (MINLP) problem for scheduling the time slots for nurses. Then, we conducted a computational experiment simulating the nurse scheduling problems during EC situations to compare the performance of the model we built and the other classic scheduling methods. Our results indicate that, compared to the other modified classic scheduling methods under crisis emergencies, our approach taking account of the BMMF in nurse scheduling during EC leads to less nurse fatigue and better balances the fatigue and the urgent demands. Given the considerable fatigue experienced by nurses in EC emergencies, this scheduling method considering fatigue can balance demands and protect nurses with less fatigue, which is highly beneficial for nurses’ well-being in EC emergency situations. Our solution offers practical strategies for meeting significant demands and reducing nurse fatigue during epidemic crises and emergencies.

Xiao Meng, Liang Ma

Conference Proceedings

Effects of Illuminance Levels on Driver Comfort: Evidence from a High-Fidelity Simulator Study

This study examined the effects of illuminance level on physiological and perceptual responses during simulated nighttime driving. Thirty licensed drivers, 15 younger (21–45 years) and15 older (65–82 years), completed three driving trials under low, medium, and high illuminance levels calibrated within roadway standards. Heart rate (HR) and subjective comfort ratings were collected to evaluate autonomic activation and perceptual experience. Results showed that illuminance level significantly affected HR, F (3, 87) = 5.94, p = 0.001. HR increased under all lighting conditions relative to baseline, but medium illuminance-maintained values closest to baseline, indicating balanced arousal. Subjective comfort ratings also varied significantly across lighting levels, F (2, 58) = 31.84, p < 0.001, with medium illumination rated highest. Age group had no interaction effect on either measure. Convergent findings identify medium illumination as the optimal level for nighttime driving, offering the best trade-off between visibility, comfort, and physiological stability.

Shene Abdalla, Siby Samuel, Amandeep Singh

Conference Proceedings

Regulation of Artificial Intelligence in Healthcare – A Global View

As artificial intelligence (AI) becomes a cornerstone of healthcare and medicine, the global focus has shifted from innovation to regulation. Across the world, efforts to regulate AI are rapidly evolving as governments and legal systems struggle to keep pace with the advances and novel applications of AI in healthcare. To support regulators and stakeholders in this task, we have examined and evaluated global AI regulatory frameworks focusing on the efforts of international organizations (WHO, EU) and individual nations (USA, UK, Australia, and Canada) to analyze the progress made in this area. While stakeholders are advancing legislation to guide AI development and deployment, gaps persist in implementation, oversight, and long-term monitoring, especially within the healthcare sector. Despite competing economic and political realities, the dilemma between centralized and decentralized policies continues to define international efforts. However, ethical standards must guide regulation, ensuring flexible yet principled frameworks that strike a balance between autonomy and human oversight. As patient data increasingly fuels AI systems, ensuring data security and patient privacy is paramount. Regulatory fragmentation, medico-legal uncertainty, and a lack of uniform best practices challenge the safe and equitable use of AI technologies. Key concerns include preserving patient autonomy, ensuring transparency, managing bias, securing data, and maintaining human oversight in medical decision-making. We suggest that future regulatory efforts be built on collaboration between stakeholders around the globe and concentrate on providing good governance, enhancing patient safety and ensuring the responsible use of AI in healthcare and medicine.

Jay Kalra, Bryan Johnston

Conference Proceedings

Organ-Specific Biomarkers of Aging: An Innovative Framework for Biological Age Assessment

As population ages, there is an immense need to identify reliable biomarkers that reflect biological age, which is representative of the cumulative burden of physiological decline across all organ systems. The current model for estimating the systemic assessment of biological age relies on epigenetic and multiomic signatures, but there remains a gap in the literature regarding the modular assessment of organ-specific aging. We describe a conceptual and evidence-based framework for evaluating organ-specific aging biomarkers across major physiological systems and integrating them with systemic aging metrics to construct a holistic assessment of biological age. We reviewed and critically appraised emerging ageing biomarkers for the cardiovascular (e.g., VO₂ max, pulse wave velocity, ApoB), hepatic (e.g., ALT, GGT, elastography), renal (e.g., eGFR, cystatin C), pulmonary (e.g., FEV1), immune (e.g., hs-CRP, CD8:CD4 ratio), musculoskeletal (e.g., grip strength, DEXA-derived lean mass), neurocognitive (e.g., processing speed, MRI volumetrics), endocrine (e.g., DHEA-S, IGF-1, cortisol rhythm), and integumentary (e.g., dermal elasticity, wrinkle depth) systems. We evaluated these biomarkers and their relationship to the trajectory of age-related decline, response to interventions, and prognostic ability for morbidity, frailty, and mortality. The overall ageing trajectory can be estimated using a tiered model that integrates organ-level biomarkers with systemic DNA methylation indices (Horvath, GrimAge, DunedinPACE), blood-based aging calculators (PhenoAge, inflammaging indices), and functional aging metrics (e.g., gait speed, reaction time, sleep architecture). This work advocates for a modular yet integrated approach to biological age assessment that captures both organ-level and systemic aging signals. As longevity medicine and preventive geriatrics advance, such frameworks may support the development of personalized interventions to extend health span, improve clinical risk stratification, and facilitate early detection of organ-specific decline before the onset of overt disease. We emphasize the importance of validated outcome measures and caution against overreliance on unverified surrogate endpoints.

Pramath Kakodkar, Nooshin Shekari, Jay Kalra

Conference Proceedings

A Participatory Design based Human-Centric Mixed Reality (MR) Simulator for Neonatal Needle Thoracentesis Procedures

Neonatal needle thoracentesis (NNTP) is a delicate, life-saving procedure performed to remove excess fluid in the pleural space in newborns. Opportunities for hands-on training are limited due to the rarity of such cases and underfunded pediatric infrastructure. To address these challenges, we developed a guided mixed reality (MR) simulator that allows medical trainees to repeatedly and safely practice NNTP in a realistic, immersive environment. By leveraging our Unity application for the Meta Quest 3 and HoloLens 2 headsets, our simulator employs passthrough technology to overlay holographic instruction onto real-world mannequins while using hand tracking to capture precise finger movements required for needle insertion. The application follows a “learn, train, test” learning model inspired by established surgical pedagogy, providing immediate feedback on procedural accuracy. Using a participatory design approach, clinical experts contributed verified procedural data and iteratively reviewed simulation accuracy to ensure medical validity. Future evaluations will compare the MR simulator’s educational effectiveness against traditional mannequin-based training, focusing on accuracy, retention, and user experience. This work demonstrates the potential of mixed reality to bridge the gap between medical theory and procedural practice, offering a scalable, cost-effective solution for neonatal care training.

Vihaan Khare, Helen Ryding, Harris Nisar, Anthony Nepomuceno, Nicole Rau, Avinash Gupta, Javed Jawad

Conference Proceedings

Bridging Expertise and Technology: A No-Code Platform for Developing Digital Psychometric Assessments

Psychometric assessments are central to diagnostics, treatment planning and progress monitoring. They provide standardised, reliable measures of mental health, cognitive performance and patient-reported outcomes, making them essential for evidence-based healthcare. Despite their importance, developing digital psychometric diagnostics is highly resource-intensive. This process requires technical expertise and access to validation infrastructures, yet many domain experts lack adequate digital tools. Consequently, development processes are slowed, economic and intellectual value is often absorbed by external parties, and many assessments never reach the market, which hinders innovation and broader accessibility. This paper introduces a digital validation platform designed to enable non-technical experts to independently design, validate and deploy psychometric assessments. Developed through a co-design approach, the platform is based on qualitative insights gained from interviews with domain experts and an in-depth analysis of seven assessment development processes. The findings highlight current challenges and inform the platform's conceptual foundation and functional design. Emphasis is placed on usability, perceived value, and integrating established psychometric methods with novel digital innovations. By reducing technical barriers, the platform enables the more autonomous, timely and diverse development of assessments, thereby fostering innovation and strengthening knowledge ownershipThis paper presents a novel digital validation platform designed to address this gap by empowering non-technical experts to design, validate, and deploy psychometric assessments independently. We propose an innovation design paradigm that foregrounds domain expertise over technical know-how following the central research question: How can a no-code platform empower non-technical domain experts to design, validate and deploy digital psychometric assessments and accelerate the development and improve the accessibility? To investigate this question, the platform is developed following human-centered design principles, incorporating insights from interviews and co-design sessions with potential users. Various domain experts were interviewed, and seven assessment development processes were closely accompanied to capture processes, challenges, and needs. The resulting qualitative findings uncover critical points in current workflows and serve as the basis for both the platform’s conceptual foundation and its functional design. This paper presents these insights alongside the platform's concept and technical architecture as well as the methodological approach for its iterative development and evaluation. The focus lies on usability, perceived value, and the ability to meaningfully integrate both traditional psychometric logic and novel data sources. The paper outlines the envisioned impact on innovation workflows, knowledge ownership, and accessibility in psychometric assessment development. By demonstrating how digital tools can democratize a traditionally centralized and analogue process, this work contributes to the field of human factors in computing. Lowering technical barriers allows for more autonomous, diverse, and timely development of psychometric assessments, driven by those who best understand the target populations and research questions. It advocates for an open as possible, accessible ecosystem where psychometric assessments can be rapidly developed, iteratively improved, and responsibly validated. These innovations and the digitalization in the field of psychometric assessments will serve as the basis for integrating more intelligent digital solutions into the healthcare system in the future, such as digital twins and personalized medicine.

Nanna Dahlem, Jan Spilski, Tobias Greff, Thomas Lachmann, Franca Rupprecht, Daniela Podevin

Conference Proceedings

Bridging the Gap of Communicating Health Information to Users: Ethically-Informed Design of App Store Description of Health Apps

Mental health has become a daunting global health challenge in both everyday situations and times of crisis. The Covid-19 pandemic has contributed to an increase in downloads and the use of these apps. Many health apps are available free of charge. As apps are used by people seeking ways to manage or augment the management of their own health conditions, app descriptions should provide clear and thorough information, ensuring that users are not compelled to seek additional sources or infer missing content. Prior research has addressed the lack of necessary information provided to users when choosing health apps in app stores, including evidence-based content on efficacy or feasibility studies, claims, target audience, privacy concerns, and hidden costs.Based on a comprehensive literature review of prior work on health apps and treatment delivery models, in the first phase of our research objectives, we introduced an app store description design to effectively bridge the communication gap in conveying health information to users. This design maximizes the impact and benefits of specific information cues (e.g., evidence-based content) to educate and support people’s decisions when selecting health apps, which can be utilized across various health app genres. Improvements in app description design could have a large impact on its use and benefits. In the second phase, we plan to conduct a usability study using a mixed-method design to evaluate the design in terms of its effectiveness, user satisfaction, clarity of information, learnability, feedback, and perceived usefulness.Our proposed design has both simple and detailed versions when needed to mitigate users’ burden, hesitancy, skepticism, and to manage their expectations when assessing what the app has to offer upfront in terms of the following aspects: free versus paid features, evidence of benefits and effectiveness, detailed cost of the in-app purchase list, subscriptions and plans, privacy information, clinical and user reviews, and pros and cons of the app. This information structure provides potential users with important details and could reduce the time and effort needed to evaluate the information provided by the app.The knowledge gained from this research could lay the foundation, contribute and shape app store description design moving forward and will help policymakers, health organizations, researchers, healthcare providers, and app developers find suitable solutions to assist users in choosing health apps.

Adel Alhejaili, James Blustein

Conference Proceedings

Orange-Sweet Aroma Reduces Stress During Numerical Tasks: A Physiological and Psychological Evaluation of Olfactory Cognition

This study examines the stress-reducing effects of the aroma of Orange-Sweet essential oil (product name) from the perspective of olfactory cognition. The research is motivated by a significant social issue: stress can hinder individuals from working and disrupt their daily lives. This research aims to clarify, using aroma, whether aroma can reduce stress within this context. The experiment was conducted in two conditions: one with Orange-Sweet aroma at the perceptual threshold, and the other above the threshold. To evaluate the stress-reducing effects of the aroma in terms of olfactory cognition, we employed multiple physiological and psychological measures during stress induced by numerical tasks. Previous studies have failed to detect clear physiological changes associated with aroma factors. Therefore, we sought to explore the underlying mechanisms of aroma-induced stress reduction by introducing different physiological measures. The physiological measures included facial thermography and electroencephalography (EEG). The psychological measures included the short version of the Profile of Mood States 2 (POMS2), "Jikakusho Shirabe, " and a Likert-scale survey assessing scent preference and intensity. From the EEG data, it was found that the Orange-Sweet aroma, at a concentration above the perceptual threshold, increased brain activity during a numerical task, leading to a state of concentration. From the psychological measures, we found that a weak aroma at　the perceptual threshold level inhibited the decline in "Vigor-Activity" on the POMS 2 and reduced "discomfort" in the "Jikakusho Shirabe". These findings suggest that even a weak aroma at the perceptual threshold may exert positive psychological effects, while a stronger aroma above the perceptual threshold may increase brain activity indicating a state of concentration. We plan to further examine the relationship between olfactory cognition and stress reduction effects using everyday relaxing aromas such as coffee and igusa (soft rush), within the same evaluation framework.

Ayaka Yamada, Toshikazu Kato, Takashi Sakamoto

Conference Proceedings

Promoting Autonomy in Older Adults with Cognitive Impairment: Co-Designing an Interactive Calendar for Memory Support

Alzheimer’s disease (AD) remains one of the most distressing public health challenges of our time, creating a critical need for tools that serve both care recipients and caregivers, especially in remote care settings. Interactive-Care (I-Care) is an innovative web-based remote caregiving platform designed to promote independence in AD patients while bridging both the physical and emotional gaps in caregiving. In this paper, we focus on I-Care’s calendar tool, developed to overcome the challenges presented by commonly used digital calendar platforms which impose high cognitive load and cause confusion among individuals with AD. We describe the iterative co-design process through which the calendar evolved, informed by multiple rounds of feedback and refinement.Participants/Methods: First, a calendar prototype was developed based on cognitive rehabilitation guidelines and existing calendar systems for individuals with mild cognitive impairment. The prototype was reviewed by experts in AD and dyads (care receiver and remote caregiver) who provided feedback and suggested modifications. The prototype was iteratively modified using this review-feedback-modification process 3 times. Next, two older adults (ages 84-88) with mild dementia (Montreal Cognitive Assessments of 19-20) participated in an iterative co-design process over the course of several interactions with the Calendar page. To quantitatively evaluate improvements, we conducted counterbalanced A/B testing comparing the pre-co-design and co-designed versions of the Calendar and additionally benchmarked its usability against Google Calendar. Participants also completed a custom Technology Acceptance Model (TAM) questionnaire that included Likert ratings (1-5, 5 being the highest) of Perceived Ease of Use, Perceived Usefulness, and Intention to Use. Results: Successive Calendar design refinements incorporated a shaded column highlighting the current day, step-by-step pop-up workflow for event creation, flashing notifications for new calendar entries, and multiple concomitant alarm options. The co-designed Calendar received high average TAM ratings in terms of Perceived Ease of Use = 5.0, Perceived Usefulness = 4.8, and Intention to Use = 5.0, indicating strong acceptance and usability. A/B testing also demonstrated substantial improvements. In the previous interface, built similarly to Google Calendar, participants were unable to complete key tasks without assistance. In contrast, with the co-designed Calendar, all tasks were completed independently, with a reduction in event creation time from 252 seconds to 94 seconds. Navigation between weeks and selecting today’s date also became faster and more accurate. Participants reported substantially higher satisfaction with co-design Calendar compared to the prior version, citing ease of navigation and clarity of visual cues. In contrast, Google Calendar task completion elicited very poor satisfaction ratings, with one participant refusing to continue using it due to its complexity.Conclusions: The I-Care Calendar design process demonstrates that individuals with cognitive impairment can engage in co-design to good effect resulting in a Calendar they can use independently. High satisfaction ratings highlight its clarity, intuitive design, and accessibility, emphasizing the value of tailoring digital tools to the cognitive needs of older adults. These findings underscore the importance of a co-design approach in developing assistive technologies that support daily routines, autonomy, and overall quality of life for older adults with cognitive impairments.

Alyssa Weakley, Sasha Pimento, Amey Gohil, Payal Hegde, Arveen Kaur, Priyanka Koppolu, Hritvik Agarwal, Preyash Yadav, Tejas Patil, Andrew Weakley, Sarah Tomaszewski Farias

Conference Proceedings

AI-Based Mobile Application for Biomechanical Assessment and Visualization of Infant Carrying Posture

Postpartum mothers are especially vulnerable to musculoskeletal disorders due to the physical demands of childcare. Inappropriate infant carrying posture frequently leads to wrist tenosynovitis, lower back pain, or pelvic strain. If unaddressed, these problems may become chronic, hinder daily caregiving, and reduce overall maternal well-being. Early intervention is therefore essential. However, until now, mothers have lacked a simple and objective tool to identify problems in their own carrying posture and to receive timely guidance.To address this gap, we developed a mobile application that provides AI-based biomechanical assessment and visualization of infant carrying posture. The application was designed with simplicity and usability in mind, requiring only a few taps to capture an image and generate immediate feedback. Five evidence-based ergonomic indicators form the evaluation framework: (1) carrying height relative to the torso, (2) closeness of caregiver–infant body contact, (3) degree of arm abduction, (4) shoulder balance, and (5) spinal alignment. Unlike conventional assessments that rely on expert observation or specialized equipment, this system offers visual feedback that compares the user’s posture with an ideal model, enabling mothers to recognize specific differences and take corrective action without requiring professional expertise.Validation was conducted by comparing system output with expert evaluations. Results demonstrated moderate to substantial agreement: Kendall’s posture (Accuracy 0.667, κ = 0.333), Infant closeness (Accuracy 0.750, κ = 0.500), Infant’s vertical position (Accuracy 0.821, κ = 0.643), and Armpit openness (Accuracy 0.857, κ = 0.714), with a macro-average of Accuracy 0.774 and κ = 0.548. These results indicate that the application reliably approximates expert judgment across multiple aspects of infant carrying posture.Equally important, user testing highlighted the impact of the design approach. Mothers reported that the app was intuitive and efficient, with assessments completed in seconds. The visualization of differences between actual and ideal posture was rated as highly useful for self-correction and for raising awareness of ergonomic carrying habits. Participants also noted the potential value of using the app jointly with professionals such as midwives and public health nurses, enhancing consultations with objective posture data and visual explanations.This study contributes to mobile health and ergonomics by translating clinical knowledge into an accessible everyday tool. It demonstrates how AI-driven posture recognition can support early intervention, helping postpartum mothers prevent musculoskeletal problems while ensuring infant safety. By combining biomechanical assessment with intuitive visualization, the application empowers caregivers with actionable feedback for healthier childcare practices.

Tamami Satoh, Nobuhiko Yamaguchi, Soraki Shiromoto, Yuki Matsunaga, Koki Nakano, Mitsuhiro Takasaki

Conference Proceedings

Neuroergonomics of Nutrition: Comparing Natural and Synthetic Sugars on Brainwave Activity and Cognitive Performance through EEG

An important area of study within neuroergonomics, nutrition plays a fundamental role in supporting brain function, cognition, and performance. For example, the brain is heavily reliant on natural sugars such as glucose and sucrose, which serve as its primary sources of energy. Glucose, in particular, supports brain activity by fueling the electrical impulses that enable communication between neurons, facilitating cognitive processes, and serving as a key component in the creation of neurotransmitters. Past research shows that the intake of glucose improves attention, memory, and problem-solving. However, synthetic sugars, including aspartame, saccharin, and sucralose, can provide the same degree of sweetness without the significant added caloric value. Although synthetic sugars are often considered substitutes for the purpose of reducing calorie intake and for being a healthier alternative with less impact on weight and blood glucose levels, their influence on cognition and brain activity is not as widely understood. This study aims to dive deeper and investigate the effects of natural vs. synthetic sugars on brainwave activity and mental efficiency using electroencephalography (EEG). The research will involve college-aged participants who will be randomly assigned to consume a beverage containing either natural sugar or a synthetic sweetener. The control group in the research will be water, which should yield negligible variation. The natural sugar group will receive drinks such as fruit juice (orange juice) and milk, both of which provide natural sugars like glucose, fructose, or lactose. The synthetic sugar group will receive beverages with artificial substitutes, such as diet sports drinks (Gatorade Zero) and diet soda. After drinking the beverages, each participant will complete simple tasks that involve cognitive focus and effort, such as Stroop tests and a less demanding memory recall exercise, while their neural activity is recorded with the EEG. Particular attention will be given to the alpha, beta, and theta frequency bands, which are closely associated with relaxation, alertness, and working memory. By comparing brainwave patterns and behavioral performance across groups, this study aims to evaluate whether natural sugars provide measurable cognitive benefits that synthetic sweeteners cannot replicate. The hypothesis is that participants who consume natural sugars will showcase improved task performance and elevated beta activity, which reflects greater alertness and cognitive function. In comparison, the participants who consume synthetic sugar are expected to show no measurable change or minimal effects on brainwave activity, as these beverages do not provide the energy for the brain to work efficiently but rather influence taste receptors, signaling only the sensation of sweetness. These expected results align with existing knowledge that glucose supports attention and memory by directly fueling neural processes, while synthetic sugars do not supply the energy required to sustain cognitive function. The significance of this study lies in its ability to extend the conversation about nutrition and cognition beyond physical health results. While artificial sweeteners are often evaluated in terms of metabolic health and blood sugar regulation, their impacts on brain activity and cognitive function are less explored in comparison. Therefore, understanding whether synthetic sugars can match or substitute natural sugars with regard to cognitive effects can provide new answers for proper dietary recommendations among students, athletes, and other professionals who rely on regular mental efficiency and cognitive function throughout the day. From a neuroergonomics perspective, the research prioritizes the intersection between human physiology and diet, along with their impact on performance in everyday scenarios. If natural sugars prove to have greater cognitive benefits in contrast to their synthetic sugar counterparts, individuals can make more conscious dietary choices for optimal mental functioning. Conversely, if artificial sweeteners prove to have comparable effects, they may offer a healthier substitute without sacrificing cognitive performance. In either case, the study aims to provide valuable insight into how nutrition influences brain activity.

Nithya Varma Madiraju

Conference Proceedings

Driving with Empathy: Understanding Novice Drivers’ Emotional Needs in Interaction with In-Vehicle AI Systems

As artificial intelligence (AI) becomes increasingly embedded in intelligent vehicles, emotion-aware human–vehicle interaction (HVI) systems have the potential to support drivers not only functionally but also emotionally. However, most existing in-vehicle AI systems are designed for experienced users, often overlooking the emotional stress and learning needs of novice drivers. This study explores novice drivers’ perceptions, expectations, and affective experiences when interacting with emotional AI in driving contexts. Through semi-structured interviews with 20 novice drivers and thematic analysis, it is revealed that novice drivers' needs for in-vehicle AI systems mainly focus on four aspects: situational awareness, behavioral guidance, emotional support, and interaction preferences. The findings reveal that novice drivers expect intelligent systems to provide not only functional assistance but also empathetic support and adaptive interaction that responds to their emotional states. This study contributes to a deeper understanding of human–AI interaction in driving contexts by highlighting how emotional safety and perceived empathy influence user trust and engagement. The insights offer practical guidance for designing adaptive and empathetic in-vehicle intelligent systems.

Wei Gong, Stephen Jia Wang

Conference Proceedings

CareBox: A Smart Modular Meal Container Integrating Human Factors and Real-Time Feedback for Preventive Health and Balanced Diets

Noncommunicable diseases (NCDs)—including hypertension, diabetes, and cardiovascular disorders—account for more than 70% of global deaths. While some cases are hereditary, most are driven by modifiable lifestyle factors, especially unhealthy diet. Many people are diagnosed only after the optimal window for prevention has passed. Embracing the principle that prevention is better than cure, this work targets diet as a direct, scalable, and sustainable intervention to improve long-term health.We present CareBox, a smart modular meal container that promotes the widely recommended 2:1:1 plate ratio (vegetables: protein: whole grains) through real-time feedback and human-factors-informed design. Following a user-centered process, we conducted semi-structured interviews with 16 adults actively engaged in chronic-disease prevention. Key pain points emerged: the cognitive burden of calculating portion ratios and the lack of immediate, actionable feedback in existing tools. Findings were synthesized using the KJ method (affinity diagramming) and translated into functional and ergonomic requirements via Quality Function Deployment (QFD).Guided by these specifications, we developed a refined prototype with three detachable compartments, each embedded with a pressure/load sensor connected to an ESP32 microcontroller. The system provides real-time, color-coded LED feedback that simulates the intended product behavior: green for 75–100% target completion, yellow for 25–75%, and red for <25%, with automatic tare to deduct container weight. The form adopts a rounded, heart-shaped geometry for emotional engagement and features a leak-proof, portable structure designed for ease of use among middle-aged and older adults.A preliminary usability evaluation indicated that the screen-free LED feedback was intuitive, reduced the cognitive load of dietary monitoring, and increased users’ awareness of food proportions. CareBox illustrates how human factors engineering, IoT-based sensing, and behavior-change principles can be integrated into everyday health products. Future work will include longitudinal field studies with at-risk populations, integration of AI-based food recognition, and broader applications in preventive healthcare.

Cheng Ming Huang, Shuo-fang Liu

Conference Proceedings

Using the SEIPS model to understand the challenges of maternity health in rural areas: a focus group study in rural counties of New York

Understanding the needs of rural women in maternity health is significantly important to develop effective policies and initiatives and to sustain care of rural communities. This study aims to understand these needs from a healthcare providers’ perspective. We conducted a semi-structured qualitative focus group involving 13 people working in different positions in a rural healthcare system in New York. Our thematic analysis was guided by the SEIPS model. Our study identified several interconnected barriers to maternity care engagement in this rural county, mainly related to the system and people components of the SEIPS. To overcome these challenges, providers and management members highlighted that it would be important to provide multilevel interventions that would help expand the transportation and childcare infrastructure and improve outreach and translation material in order to address misinformation and design tailored inclusive culturally targeted education and support systems.

Safa Elkefi, Kimberly Harry

Conference Proceedings

Lighting the Way to Ergonomics Healthcare: Eye-blink-inferred Cognitive Load and Illumination in Medical Administration Tasks

The present study was designed to undertake an investigation into the effects of varying ambient illumination levels on the cognitive load of prospective healthcare professionals engaged in a simulated medication administration task.Background: The occurrence of medication errors constitutes a significant risk within healthcare systems, with such errors frequently being attributed to the substantial cognitive load imposed upon clinical personnel. Although ambient illumination is a fundamental component of the healthcare environment, its direct influence on cognitive load remains insufficiently understood. The application of objective methodologies such as eye-tracking offers the potential for precise insights where subjective assessments may be inadequate.Design: Our experiment employed a within-subjects design.Methods: Twenty-nine nursing and medical students performed a computer-based prescription verification task under three rigorously controlled ambient light conditions: 50 lux (dim), 500 lux (standard), and 1000 lux (bright). An eye-tracking technique was utilized to record the spontaneous blink frequency of participants, serving as an objective proxy for cognitive load. The resultant data were subjected to a repeated measures Analysis of Variance (ANOVA).Results: A statistically significant main effect of the illumination condition on cognitive load was identified (F(2,56)=4.90,p=.011). Post-hoc analyses revealed that the 1000 lux condition elicited a significantly higher blink frequency in comparison to the 50 lux condition, indicating an elevation in cognitive strain under bright illumination. Critically, a more granular analysis of individual response patterns exposed a predominant "U-shaped" relationship in the majority of participants (51.7%), wherein the minimum cognitive load was observed at the intermediate 500 lux level.Conclusion: The findings of this study contest the simplistic "brighter is better" paradigm often applied to environmental design. Strong evidence is provided to suggest that while excessively bright illumination can function as a cognitive stressor, a moderate level of approximately 500 lux constitutes an "ergonomic sweet spot" for screen-based tasks. The optimization of workplace illumination is therefore presented as a critical, evidence-based strategy for the management of cognitive strain and the enhancement of patient safety.

Zhao Xuanang

Conference Proceedings

Assessment of the Relationship Between Artificial Intelligence Applications and Healthcare Workers’ Perspectives on the Future Workplace in Saudi Arabia

Healthcare is incorporating artificial intelligence (AI) more and more because it presents chances to enhance productivity, patient outcomes, and diagnostic precision. Still, there are worries about how it might affect the future of the healthcare workforce and the job security of healthcare workers. The aim of the study was to evaluate HCWs' perceptions of AI and emerging technologies in relation to their job security among healthcare workers of the family medicine centers belonging to the Royal Commission Health Service Program (RCHSP) in Jubail, Saudi Arabia.. Methodology: A cross-sectional study was conducted in the family medicine centres in Jubail, Saudi Arabia, and included healthcare workers working there. The data were collected through an online questionnaire from February to June 2024. All the participants took a two-part questionnaire that asked about demographic data and STARA awareness to determine how much workers believe these kinds of AI and technologies could replace their jobs. The research project was authorized by the Royal Commission Health Service Program (RCHSP) Institutional Research Board (IRB). Out of the 101 participants who were asked to participate in this investigation, 75% of them responded. The mean score for all items combined was 1.24 (SD = 0.14), which suggests that people are not very concerned about AI taking the place of healthcare professionals. Personal job replacement caused the least amount of concern (M = 1.12, SD = 0.91), whereas the impact on the industry as a whole caused the most (M = 1.4, SD = 1.07). These results imply that rather than endangering their specific roles, healthcare professionals believe AI will more likely change the sector as a whole. Conclusion: AI is generally seen favorably by healthcare professionals, who see it as an additional tool to supplement human knowledge rather than as a replacement. The findings show a general sense of job security, despite worries about wider organizational and industry-level effects. Maintaining trust and making sure AI integration enhances rather than detracts from healthcare delivery requires proactive workforce training, organizational preparedness, and open AI implementation strategies.

Marwan Babiker, Zenija Roja, Henrijs Kalkis

Conference Proceedings

Toward Empathetic mHealth Design for Pediatric Scoliosis: A User-Centered Inquiry

This paper presents the early stages of a design inquiry exploring what young individuals managing scoliosis actually struggle with, and how we might design digital tools to better support them. Idiopathic scoliosis affects approximately 1.7% of the global pediatric population, yet current tools available to children for understanding and managing their condition are often limited in scope, accessibility, and age-appropriateness. Rather than starting with a predefined technical solution, this research was guided by an open question: What do these young patients need? To address this, we adopted a multi-method qualitative approach including surveys (N=5), semi-structured interviews (N=10), heuristic evaluations of six existing scoliosis apps conducted by two UX practitioners, and card-sorting exercises (N=4) with participants representing diverse age ranges and condition severities. Several key themes emerged: late diagnosis, lack of clear educational resources, social stigma, insufficient emotional support, and confusion about treatment pathways. Notably, many participants had never used scoliosis-related apps, and existing apps failed to incorporate usability principles tailored for younger users, often presenting content in overly technical language lacking emotional resonance. Based on these findings, we proposed an early-stage mHealth prototype with five core areas directly addressing the uncovered themes—including features like facial blurring for privacy and simplified language for younger users. Preliminary usability testing (N=3) provided initial feedback for refinement. This paper does not claim a finalized solution, but rather contributes to design-led research by documenting how a user-centered approach can guide the development of more empathetic and accessible digital health tools for pediatric scoliosis populations.

Tejaswini Indukuri, Heejin Jeong

Conference Proceedings

Development and Usability of Tools to Improve Hospital Resiliency to Capacity Surges

Hospital capacity surges significantly affect nearly all hospitals under both routine and severe conditions ranging from seasonal flu, unpredictable admission spikes, local emergencies, and epidemics such as Covid. The inability to resiliently anticipate and adapt to these events can seriously strain bed, staff, and equipment availability, with significant associated impacts on patient care. We describe ongoing work to iteratively develop and improve usable analytic tools to help hospitals better and more resiliently predict and manage capacity surges. These models accurately project future day-to-day unit-specific room, equipment, and staff demand and shortfalls, self-tuning to any given hospital and surge pattern on a rolling basis, with re-sults displayed in intuitive and actionable manners. A key motivation is that such models, if well-designed for end-users, can help hospitals pre-emptively anticipate, prepare, and adapt appropriately locally, a fundamental concept of resiliency engineering. Participatory design, human factors, and usability analysis thus were used throughout this work to continuously improve the model’s features, interface, accuracy, and utility. Resulting functionality, model logic, and interface improvements are described, including 12%-62% improvements in all usability scores (ease of use, cognitive effort, layout navigation, time to complete, results interpretability) and 61%-95% improvements in accuracy.

James Benneyan, Michael Rosenblatt, Basma Bargal, Jasper Su, Aman Bafna, Arya Akre, Korben Wong, Aishwarya Arvind

Conference Proceedings

From Insights to Interface: Exploring Human-AI Interaction in Clinical Decision-Making for Ophthalmology

Despite the considerable potential inherent in the integration of AI into healthcare, its practical application remains limited. In a preceding study (Theilmann et al., 2025), semi-structured expert interviews were conducted to identify key factors for successfully integrating AI into healthcare. Factors identified include ease of use, alignment with clinical workflows, the incorporation of domain-specific knowledge and the involvement of stakeholders through co-design methods. This paper explores these factors in practice by implementing a low-fidelity prototype to support ophthalmologists in clinical decision-making based on optical coherence tomography (OCT) and fundus scans was implemented. It supports multimodal interaction modalities, editable AI-generated suggestions, and interactive visual overlays. To evaluate the user interface and interaction design, structured usability testing was carried out with practising ophthalmologists at a German ophthalmology clinic. The study employed a combination of quantitative and qualitative methodologies, encompassing think-aloud protocols, the System Usability Scale (SUS), and an A/B testing setup. The findings suggest that interaction design tailored to the specific needs of ophthalmology, such as visual overlays and multimodal interaction types, improves the efficiency of Human–AI collaboration. A strong preference for interpretable and editable AI outputs was identified, as these outputs allow for greater control over final decisions and increased transparency. The study outlines a human-centred design process and demonstrates how structured feedback loops, domain-specific adaptations and user-centred design can facilitate a more effective adoption of AI in healthcare. These insights could inform the development of future interactive AI systems that support, rather than replace, medical expertise.

Nanna Dahlem, Laura Steffny, Anjana Arun, Vera Marie Memmesheimer, Achim Ebert

Conference Proceedings

Epileptic Seizure Detection from EEG Data Using the Active Threshold Method

Epilepsy, defined by the WHO as a "chronic brain disease," affects approximately 0.8-1.0% of the world's population, with an estimated 1 million patients in Japan.While advances in machine learning and deep learning have improved the accuracy of epilepsy detection in recent years, high computational costs limit real-time processing. In this study, we investigated the feasibility of applying the Active Threshold (AT) method, developed for real-time voluntary eye movement detection using electrooculography (EOG), to epilepsy detection. The AT method calculates the root mean square (RMS) value from biosignals and multiplies it by an arbitrary parameter, α, to determine the threshold. This method has the advantages of real-time processing and easy calibration.In this study, we applied the AT method to electroencephalogram (EEG) data, including epileptic seizures, released by Boston Children's Hospital to verify whether an appropriate threshold could be derived. In particular, we performed a detailed analysis of the effect of changes in the α value on epilepsy detection accuracy. We selected the records of subject CHB-01 from the dataset and used preprocessed data totaling 7 hours, including epileptic symptoms. The α value was varied from 7 to 10, and the RMS calculation time was fixed at 30 seconds. In the detection evaluation, detections within the range of the epileptic seizure duration recorded in the dataset plus the 30-second RMS calculation time were considered positive, and detections outside this range were considered false.As a result, epileptic seizures were detected across all tested α parameters. However, certain seizure events within the seven-hour dataset could not be detected using any of the parameter values. These undetected seizures exhibited gradual EEG amplitude changes without significant potential amplification compared to interictal periods, making them undetectable by the AT method's approach. Additionally, noise-induced artifacts were erroneously classified as seizure events, resulting in false positive detections. Future work will need to incorporate seizure classification algorithms to distinguish genuine epileptic activity from noise artifacts in the detected EEG signals.

Daisuke Tamaki, Hisaya Tanaka

Conference Proceedings

Behavioral Change through Shared Activity Data and Future Body Prediction Using Wearable Devices in Older Adults

This study explores the behavioral and psychological impacts of a health information-sharing system that integrates wearable devices with a metaverse-based virtual environment, aiming to promote exercise and medication adherence among older adults. The proposed system allowed participants to visualize their own and others’ health behaviors—such as step counts, meal frequency, and medication adherence—via avatars acting as digital twins. These avatars reflected not only current health metrics but also projected future body composition based on collected data, thereby enhancing health awareness and risk perception. Ten participants, mainly older adults living in Gunma Prefecture, took part in a two-week intervention following a baseline monitoring phase. Health data collected through smartwatches were automatically transmitted to a tablet interface and visualized in a simplified metaverse environment. Importantly, the system was designed with minimal operational complexity—requiring only that users wear the device—thereby ensuring high usability even among first-time users of digital health technologies. Participants could passively observe anonymized avatars and data from others, fostering a sense of mutual recognition and engagement without the need for direct interaction. Statistical analysis revealed a significant increase in daily step counts after the intervention (paired t-test, p = 0.0001), while no meaningful change was observed in meal frequency (p = 0.343). Post-intervention interviews and survey results highlighted strong user satisfaction and acceptance. Participants consistently praised the intuitive interface, the motivating effect of avatar-based feedback, and the ease of use. Notably, average satisfaction scores ranged from 4.5 to 5.0 across items related to interface design, perceived usefulness, and behavioral impact—indicating that even a non-immersive, lightweight system can yield meaningful behavioral outcomes. These findings demonstrate that immersive VR is not a prerequisite for effective health promotion. Rather, simplified digital spaces leveraging mutual awareness, self-projection, and intuitive design can motivate behavioral change and enhance health literacy among older adults. This approach shows strong potential for real-world application, particularly when integrated with local healthcare services and conversational agents. The system also presents a scalable framework for future digital therapeutics targeting broader populations and specific chronic disease management.

Kenji Nakamura, Taku Obara, Mami Ishikuro, Aoi Noda, Genki Shinoda, Taeka Matsubara, Hideki Ishii, Masahiro Onishi, Yoshiaki Ohyama

Conference Proceedings

Streamline Information, Personalize Learning: Patient-Centered Knowledge Delivery for Medical Professionals

This paper presents a prototype recommendation system for personalized medical education, which leverages patient-specific diagnostic data to automatically identify and deliver relevant scientific literature on an individual basis. Diagnosis selectors extract key terms from practice management data, which are used as search queries in medical databases (e.g., PubMed). The relevance of the retrieved publications is calculated using a weighted Jaccard similarity and presented interactively on a companion tablet. The system is complemented by manually curated literature to ensure quality. Initial tests with synthetic data demonstrate the technical feasibility and potential to reduce workload in daily medical practice. By addressing the challenges of information overload and time constraints, the system offers a low-threshold entry point for continuing education tailored to the actual needs of a physician’s own patient population.

Benny Platte, Anett Platte, Rico Thomanek, Christian Roschke, Marc Ritter

Conference Proceedings

VR&R: Preliminary Results on the use of At-home VR-Therapy for Caregiver Respite and Symptom Management in Dementia

Introduction: With an aging population and rising dementia rates, there is an urgent need for affordable, personalized, at-home respite solutions. While caregivers of people living with dementia (PLwD) experience the highest levels of burden and distress, formal respite services are often costly or inaccessible. Respite interventions that both enhance mood and engagement in PLwD offer a promising way to reduce caregiver burnout. Therapeutic virtual reality (VR) is a safe, enjoyable approach that shows potential to reduce BPSD, improve quality of life (QoL), and foster social connection in PLwD and yet its potential to provide caregiver respite remains unexplored.Objectives: Here we present the preliminary results of “VR&R”, a 6-week open-label, pragmatic crossover trial with a target sample of 50 caregiver–PLwD dyads. This study aims to compare the impact of Solo versus Social VR therapy on (1) caregiver respite, resiliency, burden, and well-being, and (2) PLwD mood and BPSD, in order to inform the design of at-home VR-based interventions.Methods: Outcomes were assessed through mixed methods including standardized questionnaires, observations, semi-structured interviews, and in-app usage metrics. After VR training, dyads were randomized to complete two weeks in each VR condition (starting with Solo or Social) followed by 2 weeks of no VR access. In the Solo-VR condition, the PLwD experienced VR content independently, with the length and frequency of exposure determined at the dyad’s discretion. For Social-VR, participants co-experienced sessions with a trained research assistant skilled in supportive communication with older adults. The intervention included access to 94 360°-videos through “caregiVR”, a VR platform validated as dementia-appropriate through prior studies. The system includes a Meta Quest 2 headset with navigation and real-time casting managed via a paired Samsung tablet.Results: As of September 19, 2025, nine dyads have completed the 6-week protocol, including nine caregivers (average age 60.8 years; 66.7% female) and nine PLwD (MMSE range 7–26; average age 78.7 years; 44.5% female). VR-therapy sessions lasted approximately 30 minutes across conditions. The mean System Usability Scale score was 77.8 (range 67.5-92.5), corresponding to an “A” rating. Post-session satisfaction ratings averaged 4.5/5 stars in the social setting and 4.2/5 stars in the solo setting. Caregiver training required less than 30 minutes, and no technical support calls were reported. During VR-therapy sessions, top respite activities included socializing (e.g., phone calls, emails), completing household chores, and relaxing. Notably, 89% of caregivers were very likely to recommend VR- therapy to other caregivers.Conclusion: This is the first study to explore how VR-therapy can be used to achieve at-home respite time for caregivers of PLwD. Preliminary results suggest that both Solo and Social VR are superior to having no VR access, with caregivers reporting greater benefits from Social VR in terms of achieving uninterrupted respite time for themselves and social connection for the PLwD.

Krisha Malik, Samantha Lewis-fung, Jiamin Liang, Dhvani Patel, Joanne Berrigan, Barry Wilson Pendergast, Ron Belano, Katherine Bourolias, Lora Appel

Conference Proceedings

Responses to manual handling training and repetitive lifting: changes in spinal compression and shear forces

Manual handling (MH) is a leading cause of work-related ill-health, resulting in substantial personal and financial costs. Despite the lack of evidence to support the benefits of MH training, this remains an intervention strategy for many workplaces. Understanding reasons why MH training may be ineffective needs to be understood if work-related musculoskeletal disorders (MSD) are to be addressed. The aim of this study was to investigate the effects of prior MH training on spinal loading over the course of a repetitive handling task. Twelve male adults (mean age = 30 yr; mean body weight = 70 Kg) considered novices in repetitive MH, participated in the study. Participants attended two sessions during which they repetitively lifted (10 lifts/min) and lowered a box (13 kg) for up to 20 mins. No instructions about lifting technique were provided prior to session 1, whereas session 2 was preceded by training in recommended 'safe lifting'. Three-dimensional (3D) motion analysis and ground reaction forces provided input into a musculoskeletal model (AnyBody Technology, Denmark), used to estimate spinal loading (L5/S1 compression and shear forces). A repeated measure ANOVA (3*2) was used to determine the main effects of time (0, 10 and 20 min) and training (self-selected vs MH training) on spinal loading. A significant main effect was found for MH training on peak compression and shear forces (p=0.028 and p=0.024, respectively) when lifting, with higher peak forces in session 2 following the MH training session compared to session 1, a self-selected technique (3.29 KN vs 3.14 KN and 1.93KN vs 1.84 KN, respectively). Repetitive lifting led to decreases in cumulative compression and shear forces and increases in the slope of these curves (rate of change of loading) over time when lifting. MH training targeting ‘safe lifting’ appears to increase the risk of back injury and may discourage some individuals from adopting recommended handling practices. MH training should consider the wider context of work, challenge individuals to be adaptative to work situations, be job and task-specific, and be based on a sound andragogical rationale.

Mark Boocock, Tone Panassollo, Grant Mawston

Conference Proceedings

Understanding Office Ergonomics and Employees' Well-being in the Workplace

This study examines employees' knowledge of ergonomics, the science of designing a workplace that helps people work comfortably and efficiently. It focuses on their awareness, training, and overall well-being at work. Researchers surveyed any training involving 106 employees from different job roles to gather insights. The survey explored●How many employees know about ergonomics in the workplace●Whether they have access to ergonomic resources (like adjustable desks, chairs, or training)●How workplace well-being connects to productivityThe results revealed some critical gaps. Many employees lacked proper ergonomic training, and some companies did not have clear policies on workplace ergonomics. Additionally, while employees were interested in improving their work environment, they often did not have the necessary knowledge or resources.These findings emphasize the need for better ergonomic education and workplace improvements. By addressing these issues, companies can create healthier work environments that enhance employee well-being and productivity.

Hussien Zughaer, Nader Ghareeb, Umar Nirmal, Ammar Al Shalabi, Bader Alshuraiaan

Conference Proceedings

Comparative Assessment of Noise Exposure in Loaders’ and Bulldozers’ Cabin in Mining Industry: A Case Study

Occupational noise exposure is a significant issue in the mining industry, especially for operators of heavy machinery and it can result with serious health risks, including Noise-Induced Hearing Loss This case study analyzes the noise levels in cabins of two prevalent mining machines—bulldozer and loader, in aim to assess potential threats to operator health and to guide measures for enhancing workplace safety. Noise characteristics, such as peak sound pressure level, equivalent continuous sound level, and maximum sound level with fast weighting, were recorded during a single work shift. Descriptive statistics reviled non-normal data distribution, so the Mann-Whitney U test was applied. The results indicated statistically significant differences in all noise parameters between the two machineries (p < 0.01), with loader demonstrating elevated continuous noise levels and bulldozer exhibiting increased variability. The research underscores the necessity for specific noise reduction measures, particularly in loader cabins, to adhere to ISO 9612:2025 and ISO/11201:2010 standards and safeguard operator health. This study contributes valuable insights for occupational health assessments and serves as a foundation for future research. Subsequent research ought to build upon these findings by incorporating diverse machinery kinds and varying operating situations in order to to mitigate noise-related health risks in the mining sector.

Martina Perišić, Vesna Spasojevic Brkic, Nemanja Janev, Roberto Lujić, Aleksandar Brkic

Conference Proceedings

How Work Location Influences Task Selection and Well-being: A Qualitative Study of Hybrid Workers

Hybrid work allows employees to choose among offices, homes, and third places, yet practical guidance on matching tasks to locations and understanding well-being mechanisms remains limited. This qualitative study examines how hybrid workers select locations for tasks and how environmental characteristics shape well-being. Nineteen information workers in Japan completed semi-structured interviews of thirty minutes during December 2023 and January 2024. Inductive coding identified five task categories, mapped task–location correspondence, organized environmental factors, and classified well-being effects; mention tallies complemented interpretation. Location choice varied by task: meetings and synchronous collaboration were widely distributed; creative and conceptual work concentrated in third places; administrative and routine tasks were most often performed at home; and deep-concentration work showed pronounced individual differences. Private rooms or booths and reliable connectivity were pivotal enablers, while their insufficiency was the most frequent constraint. Third places were linked to refreshment and enhanced creativity; homes supported concentration and time efficiency; negative well-being effects were limited. Against mixed evidence on working from home, the findings support autonomy-supportive hybrid systems that treat location as a strategic resource and invest in quiet enclosed spaces and robust networks across sites.

Takumi Iwaasa, Mayu Shirakawa

Conference Proceedings

From Awareness to Action: Mapping Emotional Intelligence to Pilot Performance and Policy Reform in Aviation Mental Health

This study investigates two guiding questions: (1) Is there a gap between pilots’ mental health needs and their engagement with available institutional resources? (2) How can a focus on emotional intelligence inform educational and policy interventions in aviation?Researchers conducted a global survey of commercial pilots and air traffic controllers, which revealed minimal engagement with available support systems due to stigma and fear of career repercussions. Using Goleman’s five-component EI model, we examined international pilot competency frameworks and mapped observable pilot behaviors (OBs) to self-awareness, self-regulation, motivation, empathy, and social skills, identifying specific emotional competencies essential for adaptive performance in aviation. Results highlight the need for a systems-level redesign of aviation training and regulation that embeds emotional intelligence and resilience into human performance metrics and organizational culture.

Kimberly Perkins, Rachael Merola, Tasnim Hasan

Conference Proceedings

Embodiment at Work: A Framework for Human–Technology Interaction in the Future Workplace

Emerging technologies are reshaping the future of work, where tasks are increasingly mediated by remote, robotic, and immersive systems. As teleoperation, exoskeletons, and hybrid collaboration tools become more widespread, a central but underexamined question arises: what does it mean for humans to feel embodied in these systems? The sense of embodiment - the cognitive experience of ownership, agency, and self-location - has been shown to affect performance, learning, and well-being. Yet, its role in shaping the future of work has not been systematically theorized. Without a clear framework, human–technology systems risk being designed around efficiency rather than human experience, potentially undermining usability, ergonomics, and inclusivity.This paper offers a conceptual contribution by positioning embodiment as a key factor in understanding and guiding the evolution of work in teleoperated and hybrid environments. Three literatures provide the foundation. First, perceptual studies of embodiment in virtual environments highlight the importance of multisensory congruence but rarely address real-world labor or applied ergonomics. Second, kinesthetic learning research underscores how haptic and proprioceptive channels support skill acquisition, yet these insights remain underutilized in teleoperation and future-of-work design. Third, human factors and ergonomics research has traditionally focused on workload, fatigue, and safety, but has not integrated embodiment as a mediating construct. Together, these literatures reveal both a gap and an opportunity: embodiment can serve as a bridge linking psychological experience with organizational outcomes.The framework proposed here conceptualizes embodiment in future work settings as a multi-layered construct: (1) Perceptual embodiment, emphasizing the sensory congruence necessary for users to feel present and effective in remote or hybrid tasks; (2) Motor embodiment, focusing on the kinesthetic alignment between human operators and robotic or wearable systems, which shapes both productivity and skill transfer; and (3) Social embodiment, which extends to collaboration, trust, and presence in distributed teams. These dimensions provide a roadmap for theorizing embodiment not only as an individual experience, but also as a structural factor shaping the organization of work itself.These dimensions provide a roadmap for theorizing embodiment not only as an individual experience but also as a structural factor shaping the organization of work itself. For instance, in remote surgery, perceptual and motor embodiment influence accuracy and safety; in industrial teleoperation, they affect workload and adaptability; in hybrid conferencing and education, social embodiment determines whether remote participants feel included and empowered.This paper contributes to human factors theory and practice in three ways. First, it provides a unifying lens to connect fragmented strands of research across ergonomics, immersive systems, and the learning sciences. Second, it outlines testable hypotheses and methodological pathways - such as cross-modal congruency tasks, motion analysis, and presence measures - for empirically validating embodiment in applied contexts. Third, it highlights design implications, suggesting how adaptive control systems, wearable interfaces, and immersive platforms can be intentionally shaped to support inclusive, human-centered, and ergonomically sustainable workplaces.

Sara Falcone

Conference Proceedings

Inclusive Ergonomics in Manufacturing Processes: A Methodological Proposal for the Participation of People with High Levels of Support Needs in Workplaces

Inclusive ergonomics represents an advanced frontier of ergonomic research, aimed at designing work environments and processes that embrace human diversity, with particular attention to individuals with high levels of support needs. This paper presents the results of an applied research project conducted in collaboration between the Department of Engineering “Enzo Ferrari” at the University of Modena and Reggio Emilia, a specialized center for people with complex support needs, and a manufacturing. The objective of the research was to develop and test an operational methodology to support the inclusion of workers with high support needs within industrial production processes, using an approach grounded in the principles of inclusive ergonomics and human factors. Five factors are proposed in the design of an inclusive workplace, i.e. time, space, learning, role and self-esteem. The findings highlight that the inclusion of people with high levels of support needs, when supported by appropriate ergonomic design, employee training, and a favorable organizational context, can generate significant benefits not only for the individuals directly involved but for the entire production system. Notable improvements were observed in work quality, team cohesion, and the perceived meaning and value of work among employees. Furthermore, the adoption of inclusive practices aligns with corporate social responsibility and sustainability goals, strengthening the ethical and innovative identity of the partner company.

Lucia Botti, Chiara Arletti, Monica Bonavita, Francesco Mancini, Riccardo Melloni, Mauro Rebecchi, Ciro Ruggerini, Rosa Sammarco

Conference Proceedings

A Cross-Sector Framework for Human Factor Technologies: Comparative Analysis of Vertical and Horizontal Construction

Human factors research in construction has led to a range of technological and methodological advancements aimed at enhancing worker safety, efficiency, and well-being. However, these innovations often evolve separately within vertical building construction and horizontal transportation construction. This paper presents a comparative analysis of the two sectors to examine not only which human factors technologies can be effectively transferred between them, but also which cannot, and more importantly why. To support this, a structured decision-making framework (network diagram) is introduced to systematically categorize technologies into three transferability pathways: directly transferable, adaptable, or non-transferable. The analysis considers variations in work intensity, activity duration, and task frequency, as well as environmental and site conditions that shape human performance demands. Technologies originating in vertical building construction, such as wearable sensing systems and ergonomic assessment tools, are evaluated for their applicability in horizontal transportation construction, while transportation-based technologies such as fatigue monitoring and real-time safety analytics are assessed for potential use in vertical construction. Findings reveal both opportunities for cross-sector technology transfer and more importantly barriers rooted in differences in exposure duration, work environments, and operational logistics. The study contributes to developing a framework for adapting, refining, and contextualizing human factors technologies across diverse construction domains to advance human-centered design and safety performance.

Rezaul Karim, Usama Khan, Xingzhou Guo

Conference Proceedings

Teaming with Technology: Adaptive Automation in Joint Cognitive Systems for Industry 5.0

Adaptive automation enables dynamic reallocation of functions between people and autonomous agents to improve performance in complex work. This paper presents a meta-analysis of experimental and quasi-experimental studies (2000–2025) on joint cognitive systems in industrially relevant contexts, quantifying effects on task performance, safety/failure management, workload, trust, and learning. Across studies, adaptive automation reliably reduces operator workload and shows moderate gains in task performance and safety, with healthier trust dynamics when adaptations are triggered by human-state or event cues, made transparent to the user, and remain rapidly overridable. Risks emerge when performance-triggered switching is opaque or poorly timed, which can erode trust, induce cognitive tunneling, or hinder skill retention. The findings translate into actionable guidance for human-factors researchers, system designers, and operations leaders seeking Industry 5.0 outcomes: human-centric, resilient, and sustainable work systems in which digital teammates help people do their best work.

Jessica Johnson

Conference Proceedings

Urban Air Mobility (UAM): Preliminary Task Analysis for a Terminal Corridor Vertiport Concept of Operation

Urban Air Mobility (UAM) refers to a transportation system of cargo and passenger in urban areas. UAM concepts include a mix of onboard, remotely operated, and increasingly autonomous operations (NASA, 2017). Although mature concepts will likely employ remotely piloted vehicle operations or increasing autonomous systems to operate a fleet or network of UAM vehicles, early UAM concepts will likely employ onboard human pilots operating the vehicles (Holbrook et al., 2020). In this paper, a task analysis is presented for a potential near-term UAM concept of operation. The use case is that of terminal UAM operations in a designated corridor with the following operators: UAM fleet manager, onboard UAM pilots, Tower Controller, Vertiport Manager and Automation. The results of the task analysis highlight the roles, responsibilities, and tasks performed by different operators that can inform future design of UAM terminal operations.

Kim-Phuong L. Vu, Thomas Strybel, Vernol Battiste, Quang Dao

Conference Proceedings

Estimation of Work Productivity Using R–R Intervals and QRS Regions of Electrocardiograms during Computational Tasks under Cognitive Load

In recent years, advances in information technology have markedly increased the proportion of intellectual work across various occupations. In this study, intellectual work is defined as tasks that involve receiving and judging external information, performing knowledge processing such as analysis and numerical computation, and generating outputs. Such work demands cognitive resources that are essential for understanding, retaining, and manipulating information. However, prolonged engagement depletes these resources, leading to reduced productivity and extended working hours. Taking breaks has been recognized as an effective countermeasure, and systems capable of recommending optimal break timing are anticipated. To realize such systems, objective visualization of work productivity is required, and prior studies have investigated biological signals for assessing cognitive load during intellectual tasks. For example, Yamaguchi reported that heart rate variability analysis of electrocardiograms (ECGs) during continuous addition tasks showed increased LF and LF/HF ratios—indices of sympathetic activity—and decreased HF, an index of parasympathetic activity. These findings suggest that ECGs are effective for evaluating cognitive load. Nevertheless, no established method currently exists for quantitatively estimating work productivity in intellectual tasks using biological signals.In this study, we selected computational tasks with cognitive load as representative intellectual work and developed an estimation model (the baseline model) that predicts productivity from ECGs recorded during task performance. This model achieved an R² of approximately 0.67 and an individual error rate of about 7%. Despite these results, its accuracy was limited, and the physiological basis of estimation remained unclear. To address these limitations, SHAP analysis was applied to identify ECG waveform components contributing to the model’s predictions. The analysis revealed that the model captured overall waveform morphology and that certain heartbeats contributed more strongly than others. Specifically, high-contribution beats were characterized by shorter RR intervals (RRI), larger QRS power, and higher R-wave amplitudes compared with low-contribution beats. These findings led to the hypothesis that RRI, QRS regions, and R-wave amplitude are key features for productivity estimation. Building on this insight, participant-specific models were constructed using convolutional neural networks (CNNs) to extract morphological and temporal features from ECG waveforms. Training results demonstrated enhanced performance, with an average R² of about 0.85 and a mean absolute percentage error (MAPE) of approximately 4%, surpassing the accuracy of the baseline model. These results indicate that RRI, QRS regions, and R-wave amplitude are effective indicators for estimating work productivity during intellectual tasks.

Kosuke Sato, Yusuke Osawa, Keiichi Watanuki

Conference Proceedings

Shopfloor Terminology for Retrieval-Augmented Generation (RAG): Aligning Operator Language with Engineering Knowledge

As industrial work becomes increasingly digitalized, integrating human expertise into intelligent systems is essential for reliability and adaptability. This study investigates how curated terminology can improve Large Language Model-based Retrieval-Augmented Generation (RAG) systems for industrial knowledge management. It addresses a key linguistic issue that operators often use colloquial or locally coined terms that differ from standardized terminology found in technical documentation. This can lead to retrieval failures and inconsistent responses.A domain-specific dataset comprising 35 operator questions derived from a wire harness manufacturing manual is used to compare two types of RAG queries: natural-language operator queries and terminology-enhanced queries expanded with curated synonyms. Human evaluators assessed the correctness of generated answers. Terminology-enhanced queries achieved on average 67% correct answers compared to only 11% for nonterminology-enhanced ones.These results demonstrate the importance of terminology alignment for the reliable and effective use of LLMs in industrial contexts. Curated terminology bridges the gap between operator language and formal documentation, supporting tacit knowledge externalizationand improving retrieval reliability. This preliminary study highlights the feasibility and practical relevance of integrating terminology into RAG pipelines and outlines future directions towards adaptive, human-centered knowledge systems in manufacturing.

Ludwig Streloke, Yannick Rank, Freimut Bodendorf, Joerg Franke, Patrick Bruendl

Conference Proceedings

Design for Multi-Sensory: How Can Visual Design Help Communicate Human Sense

This critical report explores the application of multi-sensory theory within visual design practice, situated within the context of the independent research project Multi-sensory Narratives of Urban Sound: From Perception to Archive. The study begins by outlining foundational theories of multi-sensory integration and synaesthesia, which reveal the human capacity to receive and process sensory input in overlapping, interconnected ways. Through five selected design case studies, the report examines how visual design can serve as a bridge between senses, especially in projects that aim to translate auditory, tactile, or gustatory information into visual language.The paper then presents two self-directed projects, I Can Hear, In My Dream and Qiu Jiahui collected very loud noises in London made the Album it’s an Archive, in which the author explores the potential of visual design to communicate sound, evoke emotion, and raise awareness about urban noise. These projects use moving image, immersive installation, sound archives, and print publication formats to engage audiences through sight and hearing simultaneously. By critically reflecting on both theoretical knowledge and practical application, the report considers how visual design can activate empathy, memory, and bodily perception, ultimately enriching human experience through cross-sensory communication.

Jiahui Qiu

Conference Proceedings

Semi-Integral Architecture: A Strategic Perspective on Sustainable Maintenance and Repair Innovation in Social Infrastructure

In Japan, maintaining and repairing aging infrastructure has become urgent. Beyond substantial costs, the country faces a compound challenge: a shortage of engineers driven by an aging and declining population. This paper advances a strategic perspective, grounded in product architecture, for reconciling technological innovation and sustainability. Although product architecture is commonly classified as modular (high independence) or integral (high interdependence), this dichotomy fits poorly with infrastructure—such as bridges—designed for long-term service under ongoing maintenance and repair. To address this gap, we previously proposed Semi-Integral Architecture, a sustainable design concept that combines the interdependence of integral systems with the independence of modular systems, enabling partial modification and addition of components while maintaining overall system functionality. We also proposed two innovation models that capture technological change in maintenance and repair technologies: the Partial Innovation Model and the Additional Innovation Model. This study integrates these concepts and examines them through analyses of bridge improvement cases on Japan’s urban expressways. The results indicate that the Semi-Integral type serves as the structural basis for both models, confirm that the two models are used in combination, and identify the existence of spatial-constraint-induced radical innovation, whereby stringent spatial constraints trigger radical innovation. The findings further suggest that the Semi-Integral type aligns closely with Open Innovation (OI)–type collaboration and that this process provides an effective foundation for Human-Centered Design (HCD).

Atsunori Someya, Manabu Sawaguchi

Conference Proceedings

Restoring Job Satisfaction Through Subjective Well-being: Interaction Between Organizational Stress and Subjective Well-being

This study explored the hypothesis that customer service-oriented employees’ positive subjective well-being moderates the decreasing propensity of job satisfaction in circumstances where the organizational stress is perceived. A questionnaire survey was administered to 200 Japanese customer service-oriented employees. The research findings, mean, S.D., Cronbach’s alpha, and correlations for variables used in this study are shown. Then, multiple hierarchical regression is used to test the hypotheses. The results show that positive subjective well-being significantly mediates the decrease in job satisfaction propensity in stressful circumstances where employees perceive role ambiguity and role conflict.

Noriko OKABE

Conference Proceedings

Human-Systems Exploration (HSE) in Enterprise Architecture (EA): Implementing a Framework to Enhance Organizational Lifecycle (OLC) Management

Organizational lifecycle (OLC) management encompasses the creation, modification, and exchange of information throughout all phases of production. Although this effort has historically relied on project management (PM), the incorporation of technical personnel promises to increase the accuracy and reliability of the required tasks and deliverables. As organizations rapidly scale their digital footprint, an opportunity to enhance cross-disciplinary communication and collaboration is presented. Leveraging an enterprise architecture (EA) is a relatively new technique that provides a framework across domains including, but not limited to, operations, personnel, resources, and security. In respect to personnel, capturing all relevant information in an accessible repository assists PM with resource allocation based on factors such as the competencies, roles, and responsibilities of individual team members. This paper will evaluate implementation of the Unified Architecture Framework (UAF) for a small company as a real-world case study assessing the potential benefits. The UAF builds on the Department of Defense Architecture Framework (DoDAF) with the intent to include additional domains and viewpoints. In parallel with current trends, the UAF is amenable to model-based systems engineering (MBSE) which supports traceability and re-usability throughout the solution architecture lifecycle phases. Transferring document-based procedures into a virtual environment is not expected to demonstrate significant value. However, the traceability between entities enabled by MBSE will provide insights regarding the human aspect of the OLC so that operational decisions consider the entire organization and downstream effects. Leveraging the UAF to define the internal structure and processes of an organization while acknowledging personnel, the individual needs of employees, and their distinct capabilities demonstrates a progressive approach to human resource management (HRM) by integrating these facets into the holistic architecture.

Sarah Rudder

Conference Proceedings

Bridging the Digital Divide: A Design Framework for Inclusive E-Government Interfaces for Low-Literacy Users

This paper presents a framework for designing inclusive e-government interfaces tailored to the needs of low-literacy users. Underserved groups face challenges due to text-heavy interfaces, lack of multimodal properties, and poor navigability amid the growing digitalization of public services. Four key constructs were identified: simple navigation, multimodal input and output, error recovery, and cultural relevance. The peer review process and case study applications suggested potential improvements in accessibility and usability. The findings indicate clear expert agreement on aspects of clarity, task completion, and relevance; alongside constructive recommendations for enhancing multimodal properties. The proposed framework provides policymakers and designers with realistic guidelines to enhance digital inclusion and ensure alignment with the Sustainable Development Goals related to fair access to public services. Additionally, it serves as a foundation for future empirical studies aimed at enhancing inclusive e-government designs for varying literacy levels.

Waleed M Al-nuwaiser

Conference Proceedings

Corporate Social Responsibility of Indian Small Businesses: A Post-Mandate Analysis

What drives Indian small businesses to embrace Corporate Social Responsibility (CSR)—genuine commitment or mere compliance? To address this question, this study analyses CSR engagement post-mandate under the Companies Act, 2013, categorizing businesses into high, moderate, and low spenders. The analysis integrates Carroll's Pyramid of CSR, Stakeholder Theory, and Institutional Theory to examine strategic motivations and implementation patterns. Using secondary data and KMeans clustering, the study highlights significant sectoral and regional disparities in CSR spending. While businesses allocate resources towards key developmental areas, some societal and environmental priorities receive relatively limited attention. The distribution of CSR resources also exhibits regional disparities, with economically developed areas benefiting from higher allocations, while less developed regions receive relatively lower support. Findings suggest that institutional pressures—coercive (regulatory), mimetic (industry trends), and normative (stakeholder expectations)—influence CSR decision-making. The study offers recommendations to enhance CSR’s developmental impact and strengthen corporate accountability for equitable social investment.

Gulshan Kumar Yadav, Prateek Singh, Titas Bhattacharjee, Atasi Mohanty

Conference Proceedings

Digital Transformation and The Construction Industry Research Landscape: Exploring the evolving research methods

The construction industry has experienced a lot of digital transformation during the present industrial revolution. This transformation has disrupted every aspect of the construction industry. However, little attention has been paid to the impact of this technology driven disruption on the research landscape. This study aims to identify the present research methods and approaches to check for a commensurate transformation in the research landscape. To achieve this, a bibliometric review was carried out. The data for the study was extracted using keywords from the Scopus database, and the analysis was done using VosViewer. It was observed that researchers are moving more towards the adoption of mixed methods research. Also, researchers are adopting technology-driven research methods (data collection and analysis).

Samuel Adekunle, Clinton Aigbavboa, Obuks Ejohwomu, Bankole Awuzie, Andrew Ebekozien

Conference Proceedings

AI-Assisted Integrative Workforce and Capacity Management: A Use Case Report on Agile Decentralized Production Scheduling

Manufacturing companies must deal with a high level of volatility and uncertainty. Consequently, the demand for agile and decentralized decision-making in the context of production scheduling becomes apparent, since traditional rigid planning methods are failing to adapt to real-time disruptions. This paper presents a concept and architecture of a Digital Scheduling Dashboard, which is based on an autonomous scheduling process enhanced by an AI-assisted optimizer. The DSD retains Enterprise Resource Planning (ERP) systems as the authoritative baseline but delegates day-level assignment authority to assembly workers. A non-prescriptive AI-based optimization engine runs in the background, serving as a fact-checker by pre-computing complex eligibility constraints and micro-conditions (such as machine readiness, material status, qualification validity, and HSE incompatibilities) that are absent in the ERP's low granularity. The system presents workers with a pre-selected set of feasible options while reserving the final order selection as the worker’s autonomous choice. By combining employee autonomy with AI-assisted optimization, the use case aims to improve responsiveness, reduce planning overhead, and optimize resource utilization in fluctuating production scenarios.

Nika Perevalova, Stefanie Findeisen, Cedric Oette

Conference Proceedings

Adoption Barriers to Circular Business Models in Small and Medium-Sized Enterprises: A Financial Perspective

The construction sector is a significant consumer of natural resources and a major contributor to global carbon emissions, positioning it as a critical sector in the transition to-ward the adoption of circular economy (CE) principles. Small and medium-sized enterprises (SMEs) are central to this transition, given their prevalence and influence within the sector. However, they often face substantial financial barriers when implementing circular business models (CBMs). This study investigates the financial barriers to implementing CBMs, with a focus on their variation across distinct CBM types. The empirical investigation was carried out in two distinct phases. The first phase comprised 11 semi-structured interviews with representatives from Finnish SMEs, large enterprises, and public-sector organisations. The second phase employed an online inquiry to gather more targeted insights from eight participating SMEs. The preliminary findings high-lighted the interconnected nature of financial and non-financial barriers, showing that financial constraints are closely linked to market dynamics, knowledge gaps, infrastructure limitations, regulatory challenges, and risk-related concerns, ultimately amplifying strategic and operational difficulties for SMEs. The identified barriers were most pronounced for “Circular Inputs”, “Product Life Extension”, and “Resource Recovery” business models, while “Product-as-a-Service" and “Sharing Platforms” showed no distinct financial obstacles, although limited investment capacity and persistent skepticism to-ward their economic viability remained evident. The study underscored the critical role of CBM-related knowledge, sector-specific context, and targeted support measures in mitigating financial constraints and fostering the adoption of CE practices.

Paula Salonen, Marina Weck, Sariseelia Sore, Hanna Van Der Steen

Conference Proceedings

Circular Procurement in Construction: Drivers, Practices, Performance

The construction sector accounts for over one-third of global carbon emissions, making procurement a critical lever for advancing circular economy (CE) practices. However, there is limited understanding of how organizations apply CE criteria, including requirements for material reuse, life-cycle assessment, low-carbon product selection, and waste minimization, in practical procurement processes across diverse institutional and organizational contexts. This exploratory qualitative study draws on 11 semi-structured interviews with Finnish SMEs and public or large client organizations. The study employs the Drivers–Practices–Performance (DPP) framework to examine how institutional signals shape procurement behavior and influence resulting outcomes. The study findings show that CE criteria are occasionally introduced at specification but rarely influence evaluation or contracting decisions. Weak and inconsistent regulatory and client pressures mean that internal motivation often serves as the main enabler. However, fragmented responsibilities and limited organizational capacity constrain effective implementation. The study contributes to a deeper understanding of how institutional drivers and organizational practices jointly shape circular procurement performance and identifies key leverage points for aligning policy, stimulating client demand, and strengthening competences within fragmented construction markets.

Hanna Van Der Steen, Marina Weck, Sariseelia Sore, Paula Salonen

Conference Proceedings

Enabling Circular Production: Digital Business Infrastructure Adaptations

In the context of rising material costs and more stringent sustainability regulations, manufacturing companies are compelled to adopt circular value-creation models and decouple their resource consumption from the creation of high-quality products. This transition to a circular production is facilitated by R-strategies, such as Remanufacturing and Reassembly. However, the successful implementation of these strategies necessitates targeted set-up and adjustment of business infrastructures – e.g., process-related, digital, physical and logistical. In order to assist companies in the transition towards circular production, it is key to provide practical guidance for the restructuring of their infrastructures. This paper examines the digital business infrastructural changes necessary for circular production in manufacturing. A systematic literature review consolidates extant research on necessary and enabling digital business infrastructures, placing it in a shared context for restructuring. The identified findings are analyzed for their impact on a company's ability to implement circular production, mapping out key restructuring needs. To conclude, the paper emphasizes the significance of integrating digital business infrastructural restructuring into a comprehensive business transformation towards circular production.

Friedrich Wintzer, Günther Schuh, Seth Schmitz, Annkristin Hermann

Conference Proceedings

A survey of charging infrastructure users in Italy

Analyzing charging behavior is crucial for effective planning of charging infrastructure, selecting optimal charging management strategies, and implementing policies that promote electric mobility. This study focuses on the charging habits of electric vehicle (EV) drivers in urban and extra-urban areas of Italy based on a revealed preference survey. The survey explores the socio-demographic profile of respondents, revealing a higher representation of middle-aged males among EV owners. It also investigates user preferences related to charging, including power levels, battery status, and typical charging times. Furthermore, the survey considers the factors influencing the decision to purchase an EV, as well as the challenges faced with charging infrastructure and suggestions for improving the overall charging experience. The survey reveals that over half of non-EV owners plan to purchase one, though high costs and limited range remain major concerns. The key motivations for switching to an EV are saving on operating costs and reducing environmental impact. The survey also identifies common issues with public charging, such as stations being unavailable or disconnected through apps, and calls for improvements like more charging points, better interoperability, and standardized payment systemsThe survey results are essential for optimizing charging infrastructure and enhancing user acceptance of electric vehicles.

Natascia Andrenacci, Maria Pia Valenti, Valentina Conti, Matteo Gizzi, Francesca Fucile

Conference Proceedings

Behavioural Barriers Impeding Implementation of Circular Economy Practices in South African Construction Industry

The adoption of Circular Economy (CE) principles in the construction industry is widely recognized as a transformative approach to achieving sustainability, resource efficiency, and waste reduction. However, in South Africa, the implementation of CE practices within the construction sector remains limited due to various barriers, particularly behavioural factors. This paper investigates the behavioural barriers that hinder the transition towards CE in the South African construction industry. Adopting a quantitative research methodology, the study examines the attitudes, perceptions, and cultural norms influencing decision-making processes and stakeholder collaboration through structured surveys and statistical analysis. Findings reveal that entrenched resistance to change, limited awareness, and fragmented industry practices are significant impediments to CE adoption. The study further identifies a lack of alignment between industry stakeholders and insufficient policy frameworks as contributing factors. By addressing these behavioural barriers, the research highlights opportunities for fostering behavioural change through targeted interventions, education, and awareness programs. The findings contribute to the growing body of knowledge on CE implementation in developing economies and offer practical insights for policymakers, industry stakeholders, and academics. Ultimately, the study underscores the critical need for a behavioural shift to advance the adoption of CE practices, paving the way for a sustainable and resilient construction industry in South Africa.

Willington Aseni, Bankole Awuzie, Samuel Adekunle, Douglas Aghimien, Clinton Aigbavboa

Conference Proceedings

ElderEats: Simplifying Food Delivery for Elderly Users

Older adults often face difficulties when using modern food delivery applications, which are usually not designed with their physical and mental needs in mind. In this work, we present ElderEats, a mobile food ordering application created to reduce memory load and make ordering easier for elderly users. ElderEats recognizes that older adults interact with technology differently from younger generations and places their needs at the center of its design.During onboarding, the app collects information about users’ dietary preferences and restrictions, allowing it to recommend meals and restaurants that match their needs. The interface uses clear navigation, large buttons, and readable text to make the experience more comfortable and accessible. Additional features include a weekly meal planner that helps users schedule meals in advance and modify them easily when needed.To evaluate the app, a usability study was conducted with twenty participants aged 65 and above. Each participant completed the same meal-ordering tasks using both ElderEats and a standard commercial food delivery application. The results showed that participants found ElderEats easier to navigate, more comfortable to use, and better suited to their dietary and accessibility needs. They also reported higher satisfaction and confidence throughout the ordering process.The study highlights the importance of designing mobile services that address the physical and mental changes that come with aging. ElderEats shows that small but thoughtful design choices—such as guided setup, clear layouts, and personalized meal suggestions—can greatly improve the digital experience for older adults and make everyday tasks like food ordering simpler, faster, and more enjoyable.

Ankitaben Thakkar, Youngsoo Shin

Conference Proceedings

Tackling Human Factors in Aviation Safety - An Application of AI Facial Recognition Technology

Transportation accidents, are significantly affected by human factors, which account for a substantial proportion of incidents and fatalities. Factors such as fatigue, stress, illness, medication, and substance use impair pilot performance, leading to compromised decision-making, reduced situational awareness, and increased risk-taking behavior (Wingelaar-Jagt et al., 2021). While regulatory guidelines and medical evaluations exist to address these challenges, current measures often rely on self-reporting and subjective assessments that can be prone to bias. Artificial Intelligence (AI) driven facial recognition model has been used in other industries to assess human subjects’ health status (Chan et al., 2024) and cognitive workload (Iarlori et al., 2024). This research aims to develop an AI-driven facial recognition model to objectively assess pilot fitness to fly by analyzing micro expressions, facial symmetry, eye movement, and other biomarkers that reflect fatigue, stress, and impairment. The AI model will be trained using publicly available datasets containing facial images of individuals in varying conditions such as fatigue, drowsiness, stress, sadness, and under the influence of alcohol, drugs, or medication. Data preprocessing will employ facial landmark detection and attention-based image segmentation to isolate key facial regions, including the eyes (tracking movement and redness), mouth (symmetry, dryness, or tremor), and skin tone (color changes indicative of intoxication or stress)(Chan et al., 2024). Model training will leverage deep convolutional neural networks (CNNs), utilizing transfer learning techniques to enhance performance with smaller datasets. There are three tasks in this research. Task 1 focuses on model building using secondary data from publicly available facial image datasets in different conditions. Task 2 involves a laboratory-based experiment with healthy individuals to validate and refine the AI algorithm’s accuracy in detecting cognitive performance changes under stress. Participants will perform cognitive tasks under high-stress conditions, and facial images will be captured to fine-tune the algorithm. Task 3 includes a pilot simulation-based experiment to fine-tune the AI algorithm for aviation-specific applications. Licensed pilots will perform flight simulation tasks under high-workload or stressful conditions, such as emergency scenarios and adverse weather conditions. Data from facial images and simulator metrics like decision-making speed, navigation accuracy, and task prioritization will be analyzed to adapt the AI algorithm for real-time, aviation-specific assessments. The integration of this technology into preflight screening process will provide real-time, non-invasive assessments, complementing existing protocols and enhancing aviation safety by offering early warnings of performance degradation, thereby reducing accident risks and improving operational efficiency. Such AI facial recognition technology can also be utilized in-flight to detect subtle cues informing the pilot of their assessed condition. The authors would like to acknowledge Embry-Riddle Aeronautical University – FIRST Program for the funding provided. The authors would also like to acknowledge the consistent support from College of Aviation - School of Graduate Studies, and College of Engineering - Mechanical Engineering department.

Yanbing Chen, Shuzhen Luo, Andy Dattel

Conference Proceedings

The Technology Continuum on the Commercial Flight Deck and the Importance of Pilot Trust in AI

As AI continues to grow in the commercial aviation industry over the next decade it is imperative to study where and how its impact will be needed. While some areas like aviation maintenance need immediate implementation of AI to relieve maintenance personnel shortages, other areas like the flight deck could also benefit greatly from using AI. However, involving AI in commercial flight requires the trust of the pilots when using AI. This research defines AI and the needed trust that must go with it on the commercial flight deck from the perspective of a commercial flight deck technological continuum to show where AI has its origins, where it is now and where it will eventually find its place in the future. While the continuum shows how important it is for the pilots to work with AI to make efficient and safer decisions, it also clearly shows how vital pilot trust is in the AI as it is infused in the technology continuum over time. With the continuum analysis complete, the researchers then present the results of a recent commercial pilot trust in AI survey. The survey involved over 220 pilots to analyze where commercial pilot trust in AI currently stands as new AI technology continues to advance on their flight decks.

Mark Miller, Sam Holley, Leila Halawi, Matt Mclaughlin

Conference Proceedings

Evaluating Simple Vibrotactile Feedback for Manual Glideslope Landings in Urban Air Mobility Simulation

Urban Air Mobility (UAM) is a transportation system that integrates vertical takeoff and landing (VTOL) aircraft into the National Airspace System, with the goal of transporting passengers and small goods within metropolitan areas. Although the vehicles are capable of VTOL, a glideslope landing approach was studied due to its advantage over VTOL in air traffic management coordination, energy consumption and passenger comfort. This study evaluated whether vibrotactile feedback improved manual glideslope landing performance when applied to the wrist on the dominant versus non-dominant arm. Participants performed glide slope landing using recommended flight parameters provided on a glideslope display to descend and land at a vertiport. Using a CAVE virtual reality simulation, sixteen novice, non-pilot participants completed 18 simulated landings at three different vertiports along two arrival entry routes (clockwise and counterclockwise direction) under three tactile feedback conditions: no feedback, feedback on dominant arm, and feedback on non-dominant arm. Performance data and subjective ratings of workload, usability, and situational awareness were collected. There was no significant effect of feedback condition. However, participants found the wrist placement for the vibrotactile alerts to be comfortable and suggested that dynamic vibration cues could further improve guidance from the alerts. Additionally, participants made more forward speed errors when landing at specific vertiports in the clockwise direction, which may have been due to the route characteristics that increased the difficulty of maintaining a consistent forward speed. These findings suggest that route design is a critical factor to consider when planning the approach paths for UAM operations, and could inform future tactile feedback design for enhancing pilot performance.

Danny Sarmiento, Vannessa Nguyen, Vinicius Dugue, Kim-Phuong L. Vu, Thomas Strybel, Vernol Battiste, Praveen Shankar, Panadda Marayong

Conference Proceedings

Goodbye to Parking Anxiety: An Empirical Study on the Usability of Automatic Parking Assist Systems

Parking continues to pose a considerable challenge for novice drivers, particularly within complex urban settings. As vehicle automation technologies advance rapidly, Automatic Parking Assist (APA) systems have become increasingly prominent and valuable features in contemporary vehicles. Despite their growing prevalence, empirical research on the user experience of APA systems remains scarce, and a systematic evaluation framework has not yet been fully established. To address this gap, the present study conducted a comprehensive evaluation of the usability and performance of APA systems in three mainstream sport utility vehicle (SUV) models available in the Chinese market: the Avatr 11, AITO M5, and Trumpchi Emkoo. A mixed-methods approach was employed, integrating heuristic evaluation with objective performance testing. Specifically, two performance metrics were collected, including parking time and the number of steering corrections, in order to assess the systems across three common parking scenarios. The usability issues identified through heuristic evaluation were categorized into four dimensions: functionality, interactivity, sensory experience, and emotional response. These dimensions served as the basis for analyzing the frequency and characteristics of usability problems and for informing future design improvements. Additionally, a user journey map was constructed to represent the operational flow of APA usage. Finally, targeted design recommendations are proposed to enhance system satisfaction. These insights contribute to the refinement of APA systems and offer a practical framework for future research on intelligent vehicle-human interaction.

Jin-long Lin, Chen-rao Zhong, Meng-Cong Zheng

Conference Proceedings

Analysis of Energy-efficient Operation Characteristics of Express Trains using Global Navigation Satellite System Data

To reduce railway energy consumption, optimizing driving techniques is an effective approach, along with enhancing energy-efficient rolling stock and infrastructure. To identify operation characteristics associated with lower energy consumption and to investigate the relationship between driving speeds and energy consumption, this study analyzes operation performance data.Data were gathered over one year from GNSS-equipped tablets. Analysis focused on one segment between two scheduled stops of the same limited express train. After excluding data reflecting deceleration due to signal aspects or departure delays of ≥1 min, 95 out of 331 data points were used. Excluding outliers, two groups were formed based on estimated energy consumption: the lowest 25% classified as the "low group" and the highest 25% as the "high group." Energy consumption was estimated using GNSS data by considering running resistance and track gradient. T-tests were conducted to assess significant differences between the two groups for mean estimated energy consumption, travel time, and driving speeds at three locations: (1) before the speed restriction zone in a downhill section, (2) before the speed restriction zone in a flat section, and (3) before the station stop. The locations chosen for comparing driving speeds were characterized by remarkable speed variability, situated before the speed restriction zones and the station stop requiring deceleration. This was based on the premise that shortening acceleration time and lower speeds prior to braking generally enhance energy efficiency and greater speed variability may reflect differences arising from driving techniques.T-test results indicated that the mean estimated energy consumption of the "low group" (136.1 kWh) was significantly lower than that of the "high group" (150.0 kWh). By contrast, the mean travel time of the "low group" was significantly longer (low: 13 min 34 s, high: 13 min 3 s). Regarding driving speeds at the locations, the "low group" displayed significantly lower speeds: (1) before the speed restriction zone in a downhill section (low: 102.2 km/h, high: 108.5 km/h); (2) before the speed restriction zone in a flat section (low: 114.5 km/h, high: 120.9 km/h); and (3) before the station stop (low: 111.0 km/h, high: 122.2 km/h). The reduced speeds prior to the onset of braking during low-energy-consumption runs corroborated theoretical expectations. Despite the "low group" having a longer mean travel time, it remained within the scheduled travel time (13 min 45 s). Regarding energy-efficient operation characteristics, the "low group" exhibited greater variability in driving speeds at locations (2) before the speed restriction zone in a flat section and (3) before the station stop. Accordingly, train performance graphs illustrating driving speed versus travel distance for the "low group" were examined. Two energy-efficient operation patterns emerged: (A) increasing speed during the midsection and coasting after the speed restriction zone before the station stop and (B) coasting during the midsection and accelerating after the speed restriction zone before the station stop. Pattern A was frequently observed among train drivers who were conscious of energy-saving practices. Interviews with train drivers indicated that pattern B was favored to prevent deceleration caused by signal aspects.

Tamaki Ueda, Daisuke Suzuki, Chizuru Nakagawa, Tomoyuki Ogawa, Hiroyuki Sako, Yuuta Yamamoto

Conference Proceedings

The Role of Health and Cognitive Resilience in Transportation

Human performance is a critical pillar of safety in modern transportation systems. Whether in aviation, rail, maritime, or road operations, the ability of personnel to manage high workloads, unexpected disruptions, and long-duty hours relies heavily on both their physical health and cognitive resilience. As transportation systems become increasingly complex and interconnected, understanding and supporting the physiological and psychological readiness of human operators is essential. This paper explores the intersection of health, cognitive resilience, and emerging technologies—specifically the role of wearable devices and affective computing—in enhancing human performance across safety-critical transport domains.Health and cognitive resilience are deeply interlinked. Operators suffering from fatigue, poor sleep hygiene, stress, or underlying health conditions are more prone to errors, reduced situational awareness, and impaired decision-making. Cognitive resilience—the capacity to adapt, focus, and recover during high-pressure or unexpected situations—is increasingly recognized as a core competency for transportation personnel. Through field studies and case analyses, this paper highlights how cognitive lapses often correlate with degraded health conditions, both of which are rarely detected by traditional supervision or self-reporting alone.The integration of wearable technologies offers a promising solution. Devices capable of continuously monitoring heart rate variability, sleep patterns, fatigue levels, hydration, and stress indicators are enabling real-time assessments of operator readiness. Paired with intelligent data interpretation, these wearables are no longer just passive trackers but active tools in predictive safety management. For instance, a wrist-worn device detecting elevated physiological stress during pre-flight checks could alert supervisors to intervene early, helping to avoid errors or escalation.Beyond physiological metrics, social and affective computing expands the monitoring scope to emotional and cognitive states. Using facial recognition, voice pattern analysis, and behavioral cues, these systems can estimate affective load, detect early signs of burnout or anxiety, and support more nuanced decision-making around task assignment and crew pairing. Affective computing can also be embedded in simulators and training environments, offering personalized feedback on stress responses and emotional regulation under simulated high-stakes scenarios.Importantly, the paper emphasizes a human-in-the-loop approach, where technology augments—not replaces—professional judgment. Ethical considerations around privacy, consent, and the use of biometric data are also addressed, advocating for transparent protocols and employee involvement in system design and implementation. Resistance often stems from fears of surveillance or punitive use of data, so building trust is essential for long-term adoption.Ultimately, promoting operator health and cognitive resilience—supported by wearable and affective technologies—creates a safer, more adaptive transportation workforce. Organizations that embed these practices into their safety culture, training programs, and operational policies not only reduce risk but also enhance workforce sustainability and job satisfaction. In the future of transport, where humans and machines increasingly collaborate, understanding and supporting the human condition will be just as vital as optimizing the technology itself.

Debra Henneberry, Dimitrios Ziakkas, Anastasios Plioutsias

Conference Proceedings

Evaluating Customer Loyalty and Sustainability Performance of the TPASS Integrated Commuter Pass in Taiwan

This study evaluates the sustainability performance and customer loyalty of Taiwan’s TPASS Integrated Commuter Pass, a national transportation initiative jointly supported by central and local governments to encourage public transport use and reduce private vehicle dependency. The TPASS system, launched in 2023, integrates metro, light rail, intercity and city buses, Taiwan Railways, and public bike services under a region-based flat-rate fare structure. It represents a significant policy effort to improve multimodal integration, enhance accessibility, and promote equitable urban mobility across metropolitan areas. By combining multiple transportation services under a single pass, TPASS seeks to lower commuting costs, simplify transfers, and create a more sustainable and human-centered transport environment.A structured questionnaire survey was conducted among TPASS users in northern Taiwan, where ridership density and intercity commuting are particularly high. The questionnaire, designed through a review of relevant literature, measured perceptions related to sustainable transportation, urban sustainability, and customer loyalty using a five-point Likert scale. The study adopts a mixed quantitative approach combining Importance–Performance Analysis (IPA), the Improvement Coefficient (IC), and regression modeling. IPA was used to identify which attributes users consider both important and underperforming, providing guidance for priority improvements. The IC was applied to quantify the gap between importance and satisfaction, enabling policymakers to rank service attributes objectively. Regression analysis was then used to examine how perceived importance and satisfaction within sustainability dimensions influence users’ continued use and loyalty toward TPASS.The results indicate that TPASS has contributed positively to economic sustainability and system integration, yet several aspects particularly mobility, affordability, and fairness—require improvement. These attributes were located in the “Concentrate Here” quadrant of the IPA matrix, signifying high importance but relatively low satisfaction. Conversely, efficiency and accessibility were categorized in the “Keep Up the Good Work” quadrant, indicating areas of strength that align with user expectations. Regression outcomes further show that perceived performance and fairness exert a significant influence on both satisfaction and continued use intention. These findings underscore the importance of not only operational efficiency and fare integration but also the social inclusiveness and equity of the system.Overall, this study provides empirical insights from the user perspective on how integrated fare policies can advance sustainable urban mobility. By combining IPA, IC, and regression methods, it demonstrates an effective analytical framework for prioritizing improvements in transport policy. The results suggest that enhancing connectivity, maintaining affordable pricing, and ensuring equitable access are essential strategies for achieving both long-term financial viability and human-centered urban sustainability. TPASS thus offers a meaningful reference for other regions seeking to design integrated commuter passes that balance efficiency, inclusiveness, and sustainability.

Jung Yeh, Xing-wei Liu, Chia-hui Lee, Hsiang-chuan Chang

Conference Proceedings

The Role of Advanced Air Mobility in the Future of Safe Transportation

As urban centers grow more congested and surface infrastructure reaches its limits, the global transportation landscape is turning toward Advanced Air Mobility (AAM) as a transformative solution. AAM refers to a new class of air transportation systems—including electric vertical take-off and landing (eVTOL) aircraft, autonomous aerial vehicles, and integrated airspace management systems—designed to move people and goods safely, efficiently, and sustainably within and between urban, suburban, and rural environments. This paper explores the evolving role of AAM in shaping the future of safe, multimodal transportation, with a focus on human factors, system integration, and operational risk management.AAM introduces both opportunities and challenges for transportation safety. On one hand, it offers the potential to reduce road congestion, lower emissions, and provide rapid emergency response capabilities. On the other, it brings new complexity in terms of airspace coordination, pilot-automation interaction, and community acceptance. Drawing from current testbeds, pilot programs, and regulatory frameworks, this paper assesses how AAM can be designed and deployed to enhance—not compromise—transportation safety at scale.Human performance remains central to AAM safety, particularly during the transition from piloted to increasingly autonomous operations. AAM systems must be designed with a human-in-the-loop or human-on-the-loop architecture, ensuring that operators, controllers, and maintainers maintain adequate situational awareness and decision-making authority. Lessons from commercial aviation and unmanned systems emphasize the need for trustworthy automation, transparent interfaces, and robust training programs that prepare both professionals and the public for this new form of mobility.Equally critical is the safe integration of AAM into existing airspace. Urban Air Traffic Management (UTM) systems must balance flexibility and control, allowing for dense operations without increasing collision or incursion risks. This paper highlights the role of AI-enabled traffic coordination, real-time risk modeling, and communication protocols designed for high-density, low-altitude airspace. Collaboration between civil aviation authorities, municipal governments, and industry stakeholders will be essential to establish performance-based regulations that prioritize safety while enabling innovation.AAM’s safety narrative also includes ground risk mitigation, emergency preparedness, and public confidence. Vertiport placement, environmental noise considerations, and emergency landing protocols are not peripheral details—they are foundational to safe deployment. Community engagement and education will be crucial in building social trust and ensuring equitable access to AAM services.In conclusion, Advanced Air Mobility holds transformative potential for the future of transportation—but its success hinges on how safety is engineered into every layer of its development. From airspace integration and human-machine interaction to public engagement and operational resilience, AAM must be approached as a socio-technical ecosystem. By embedding safety from the outset and aligning technological innovation with human-centered design, AAM can become a cornerstone of future transport networks that are not only faster and more sustainable, but fundamentally safer.

Dimitrios Ziakkas, Debra Henneberry, Konstantinos Pechlivanis

Conference Proceedings

Digital Copilots: Advancing Pilot Mental Health Through AI Chatbots and Systems

Commercial pilots routinely work long, irregular schedules under high-stress conditions, and these demands are linked to higher rates of anxiety, depression, and fatigue than those seen in the general population. Persistent mental health stigma within the aviation community often drives pilots toward self-reliance rather than professional care due to the possibilities of suspension, grounding or loss of their pilot license. Recently developed AI-driven mental-health chatbots could offer pilots an anonymous support option that circumvents this stigma. Although no aviation-specific trials exist, a number of studies in the general population report moderate symptom reductions from use of mental health AI chatbots. In this review, we synthesize that evidence and evaluate its applicability to commercial pilots. For this literature review, we reviewed over 100 papers using terms for aviation psychology, pilot mental health, and digital/chatbot interventions; we also screened literature on complementary AI systems. Overall, findings suggest AI chatbots are suitable as adjunct support for pilots with mild or subclinical distress but should not replace professional care in severe cases. Chatbots may facilitate self-screening, early detection, and brief preventive coaching for pilots. In addition to chatbots, we also review other AI-based systems to understand their impact on the mental health of commercial pilots. Preliminary evidence also suggests that chatbots and other AI systems may enhance emotion-regulation skills, which could contribute to overall improved operational safety. When access to human therapists is limited, these interventions could offer discreet, scalable mental health support tailored to the unique demands of the aviation profession.

Yihao Zheng, Dina Kaur Chawla, Kimberly Perkins

Conference Proceedings

Supporting Users' Understanding of Driving Automation Systems: The Effect of Meaningful System Names and Responsibility-Focused Textual Reminders

Implicit demands on drivers are growing with today’s available variety of sustained driving automation systems: Drivers must understand each system’s function, limitations and their own corresponding role. Compared to the strong emphasis on the technical requirements of driving automation (e.g., outlined in international provisions concerning the approval of vehicles equipped with driving automation systems), the process by which users learn about and adapt to their emerging roles have not been explored to the same extent. This study examines how Human-Machine-Interface (HMI) design can enhance drivers’ role understanding as part of mode awareness across different SAE levels of driving automation, and was conducted in preparation for a larger on-road study. Thirty-seven lay participants were assigned to one of two sets of HMIs for different sustained driving automation systems: An informative HMI, including meaningful system names and responsibility-focused textual reminders, versus a non-informative HMI. Participants then answered questions regarding their responsibilities and permitted behaviors when using the different systems. Overall, results show that participants in the informative HMI group gave significantly more correct answers about their user role and behavioral possibilities than participants in the non-informative HMI group. The informative HMI also supported participants in correctly ordering systems by automation level. The findings of this study are used in an upcoming on-road study to examine a novel assessment method for mode awareness. Future research could further examine users’ role-related information needs and the most effective ways to convey this information via the HMI.

Emma Czupi, Elisabeth Shi

Conference Proceedings

Raising Awareness for Confusion - Stimulating the Discussion About Robustness of Mode Awareness Assessment in Automated Driving

Mode awareness is a complex psychological construct concerning the awareness of the currently active mode in an entire multi-mode system such as vehicles equipped with driving automation systems. The article aims to stimulate scientific discussion around the test quality of mode awareness assessment related to driving automation. Often, behavioral metrics are examined to draw conclusions on drivers’ understanding of the driving automation system and its status. Here, behavior deemed adequate for the active driving mode is subsequently attributed to mode awareness, while mode inadequate behavior is attributed to mode confusion. Besides cognitive representation of information, other influencing variables can contribute to the observed behavior. Considering basic psychological processes of information processing and action selection, the authors highlight how alternative explanations for observed behavior emerge. The authors advocate following recent approaches of combining metrics to capture human-machine-interactions holistically and draw more reliable conclusions while ruling out alternative explanations.

Lena Plum, Elisabeth Shi

Conference Proceedings

Evaluating the Impact of Haptic Cueing on Training Effectiveness in a Helicopter Roll-Tracking Task

Helicopter control presents significant challenges due to the system’s inherent instability and the high cognitive demands placed on the pilot. This study investigates the effect of haptic cueing on training effectiveness in a VR simulator-based compensatory roll-tracking task. Task difficulty was varied through changes in system dynamics, stability, and task type. Haptic cues, designed using McRuer’s crossover model, were delivered via a vibrotactile suit to provide intuitive feedback on roll-angle errors. Performance was assessed using mean squared error, training time, and Bedford Workload Questionnaire. Results showed improved performance for the haptic-trained group, particularly as task difficulty increased, with minimal impact on training time. Although data variability was high due to the small sample size, findings indicate that haptic cueing enhances training in complex control tasks.

Marwan Gomaa, Gabriele Luzzani, Michael Morcos, Rachit Raval, Umberto Saetti

Conference Proceedings

Design Principles for Human–Autonomy Interaction in the Maritime Domain: Transition from Onboard to Remote Operations

As the maritime industry transitions toward highly automated and autonomous operations, the roles of human operators, remote control centers, and decision-support technologies are evolving rapidly. In this changing context, particularly from onboard operation to remote operation of multiple autonomous vessels, ensuring safety, usability, and resilience in safety-critical systems requires the effective application of human factors (HF) and human-centered design (HCD) principles. This paper presents a literature review in the maritime sector to identify key design principles that integrate human factors into highly automated operations. The aim is to explore how HF and HCD principles can support the transition from traditional onboard maritime operations to remote operation systems, ensuring safety through successful design strategies. The review draws on eight academic publications (years 2013-2025) that were systematically filtered to include empirical studies addressing HF and HCD applications in maritime automation, with particular attention to Maritime Autonomous Surface Ships (MASS), remote operation centers, and integrated decision-support systems. These publications were selected based on clearly defined inclusion criteria and screened for HF and HCD principles, outcomes, gaps, and implications. The results show that current practices address a range of micro-, meso-, and macro-level HF principles and design implications. It was found that both research and practical application remain limited and lag behind the rapid technological developments within AI and automation. Across the literature, consistent gaps remain in the study of HF and HCD for MASS and remote operations. These gaps highlight the complexity of transitioning from onboard to shore-based operations and underscore the need for a sociotechnical perspective that spans individual, team, and system coordination, as well as organizational and ecological contexts.

Mina Saghafian, Jooyoung Park, Stine Thordarson Moltubakk, Lene Elisabeth Bertheussen, Stig O Johnsen, Ole Andreas Alsos

Conference Proceedings

A Structure Aware GAN-Based for Ancient Chinese Calligraphy Style Transfer

A written Chinese brush calligraphy is an artistic style. Calligraphers use brushes to write characters for their artistic expressions and creations. They exhibit various structures and attractive stroke shapes. Unfortunately, brush-written characters of ancient masterpieces are getting unclear or damaged if their papers or steles decay. Unlike the alphabet, each Chinese character has its own significance and completion. Besides, calligraphers are used to write non-standard character forms for some Chinese characters. The different character forms are due to handwriting habits. Therefore, we propose a novel mechanism that reduces the effect of non-standard character forms by normalizing the loss value of the generated Chinese calligraphy. A generated calligraphic characters strictly followed the features of the structures and stroke shapes that is challenging for the generation procedure.Recently, the development of convolutional neural networks (CNNs) has enabled to generate font automatically. Some attempts have been made to learn font generation and achieve talented results. Many researches with deep neural networks have generated whole sets of alphabetic languages. So far, some researchers provided strokes and radicals to assist Chinese-font generation. However, only a few studies have focused on ancient Chinese calligraphy generations. This paper proposes a structure aware consistency framework to provide structural correction for the generative model. According to ancient Chinese calligraphy copybook, we developed a method for multi-style calligraphy transformation. An inverse mapping network is used to automatically supervise the structural correctness of forward-generated Chinese calligraphy characters. Finally, the proposed method generated Chinese characters that ancient calligraphers had never written. In generative Chinese calligraphy, previous works provide productive results. However, their approaches usually rely on manual intervention such as radical decomposition. Any manual annotated data is labor intensive. Sometimes they fail in the stroke shapes or generate the incorrect structures for calligraphic characters. To address these problems, our proposed inverse mapping architecture to penalize incorrect structures of the generated characters. The inverse mapping architecture improves the applicability of our framework. We also provide an overlooked mechanism to decrease the adverse effect of non-regular character forms. The overlooked mechanism normalizes the L1 loss of a generated character by multiplying an overlooked weight. This mechanism brings an interesting aspect of Chinese calligraphy generation. The contributions of this study are as follows: (1) A proposed structural consistent architecture to overcome the most challenging aspect of generating calligraphic fonts. This architecture guides the structural correctness of the forward generative characters and significantly improves the applicability of our framework. (2) To reduce the influence of calligraphic characters which are different from standard character forms. We normalize the loss value of generated characters using an overlooked mechanism. Our one-to-one transfer model tends to overlook the calligraphic characters that are non-standard character forms during training.

Derlor Way, Meng-zhe Cai, Zen-chung Shih

Conference Proceedings

From Checklists to Chatbots: Reimagining HRA with Generative AI

This paper evaluates the capability of Large Language Models (LLMs) to support Human Reliability Assessment (HRA) through a systematic test using the Integrated Human Event Analysis System for Event and Condition Assessment (IDHEAS-ECA) methodology. Using Claude Opus 4.1, we generated Steam Generator Tube Rupture scenarios and subsequently tasked the model with producing a comprehensive HRA analysis, which was then independently reviewed by two IDHEAS-ECA method experts. The LLM demonstrated substantial domain knowledge, generating technically coherent scenarios with appropriate procedural details and system responses, and produced a structured analysis covering cognitive functions and performance influencing factors. However, expert review identified critical methodological gaps including conflation of concepts from different HRA methods, omission of formal task analysis steps required by NUREG-2256, and inadequate human failure events identification. While current LLMs show promise as auxiliary tools for scenario generation and preliminary analysis, they require significant enhancement before supporting safety-critical HRA applications. Future work should focus on method-specific training, integration with structured knowledge representations (e.g. knowledge graphs), and development of validation protocols to ensure appropriate application boundaries.

Michael Hildebrandt, Awwal Arigi

Conference Proceedings

Combining large language models with linguistic features for the readability complexity assessment of texts

Readability is a fundamental skill for students, as the acquisition of knowledge throughout formal education is mediated largely by text. Texts vary widely in their complexity, and assessing whether a text is appropriately complex for a given reader is essential in educational contexts. While traditional approaches to text complexity rely on shallow surface proxies, such as word frequency, sentence length, or lexical diversity, these features fail to fully capture cohesion and coherence, two central dimensions of readability. Moreover, comprehension depends not only on textual features but also on the reader’s common knowledge, which remains difficult to approximate computationally. This work addresses these challenges by proposing a hybrid computational linguistics framework for text complexity assessment that integrates classical readability measurements and leverages large language models (LLMs). Our contributions are threefold:First, we develop a novel set of linguistic features designed to approximate cohesion and coherence more effectively than traditional shallow measures. These features are based on discourse patterns, lexical distribution, and semantic similarity between text segments, leveraging embeddings from transformer-based models. Specifically, we introduce new coherence features derived from segmentation heuristics and sentence embeddings, including measures of givenness, lexical diversity based on word distribution, and relative semantic distances across segments. These features capture how information is introduced and developed across a text, thus reflecting its readability at a deeper level than word counts or syntactic proxies alone.Second, we design a hybrid approach that combines these linguistic features with a fine-tuned LLM acting as a common knowledge assessor. While the linguistic features model structural and semantic regularities internal to the text, the LLM contributes an externalized knowledge base that helps approximate the background knowledge readers may bring to comprehension. By treating the LLM’s judgments as an additional feature set, we establish a hybrid model that integrates the strengths of both paradigms.Third, to support reproducibility and further research, we compile and release a new corpus of Spanish educational texts, drawn from the Chilean school system, annotated with grade-level labels. The dataset contains 656 texts spanning grades 1 through 8, and we provide detailed linguistic feature extractions alongside the labels.Our experimental evaluation compares three approaches: a fine-tuned LLM (GPT-4o), a machine learning model trained solely on linguistic features, and the proposed hybrid model. Results show that the LLM alone performs poorly on this task (accuracy = 0.18), whereas the linguistic features model achieves a substantially higher accuracy (0.61). Most importantly, the hybrid model outperforms both baselines, achieving 0.75 accuracy, thereby demonstrating the complementary value of combining linguistic insights with LLM-based judgments. Feature analysis further shows that our proposed measures, including KL divergence, lexical diversity, semantic distances, and givenness, are among the strongest predictors of text complexity, highlighting their explanatory power.In summary, this work advances the state of the art in text complexity assessment by proposing new semantic-based readability features, integrating them with LLMs to approximate reader knowledge, and validating the approach on a novel educational corpus. The findings demonstrate that hybrid models are not only more accurate but also more theoretically aligned with multidimensional views of text comprehension, bridging computational linguistics with educational applications.

Diego Palma, Christian Soto

Conference Proceedings

Effects of Threat Visibility and Geographic Knowledge on Attention Allocation During BVLOS Drone Operation: Using Gaze Transition Entropy

Beyond visual line of sight (BVLOS) is considered a key feature of next-generation drone operations. The design of Ground Control System (GCS) interfaces should incorporate Geographic Information System (GIS) capabilities to enhance the operator's decision-making process and support effective DAA procedures, especially when dealing with unexpected events during BVLOS flights.Our previous study has investigated the impact of different map representations and geographic knowledge on drone operators' information processing and decision-making in BVLOS drone operations with different emergencies through eye-movement analysis with pupil diameter, and the number of fixations and their ratio per Area of Interest (AOIs) consist of FPV camera view and GCS. The results show that only the map type has a significant difference in pupil diameter, and the group with geographic knowledge and more instant geographic information, using satellite map, had larger pupil diameter. Moreover, this previous study implied that the risk assessment could rely on visual identification, location fixation, and threat predictability of obstacles.However, eye tracking analysis using only pupil diameters and fixations could not accurately certify the exact movement and transition of eye movements between AOIs.Gaze transition entropy (GTE) and stationary gaze entropy (SGE) are the indicators of focused attention and goal-directed behavior for reading the information processing with eye movement data. GTE measures how systematically or randomly gaze moves between displays or information sources (AOIs). High GTE indicates random transitions between AOIs, leading to high cognitive load. SGE indicates the uncertainty about the distribution of AOIs on which gaze is focused over a given time. High SGE indicates widespread gaze across AOIs, while a low SGE indicates a focused focus on a specific AOI.Therefore, GTE and SGE were analyzed to better understand cognitive information processing using the same dataset. The data collected from 40 participants (M=24.6, SD=5.55) were divided into four groups with two between-subjects factors: whether the geographic instructions were given prior to or not, and map type (Road or Satellite). They conducted four scenarios with various emergency occurrences: bird approach, flock of birds, strong wind, and fire in a building on the route. Additionally, scenarios' influence is validated, which the previous study did not.We hypothesized that (1) Map type affects GTE and SGE; (2) The absence of instruction doesn’t affect GTE and SGE; (3) Scenario affects GTE, SGE, and gaze transition probability.Results showed that GTE and SGE were not significantly varied across map types, geographic instruction, and scenarios, although the group with instructions and satellite map shows highest GTE. On the other hand, it is revealed that the gaze transition probability from AOI 1 (FPV) to AOI 2 (GCS) and from AOI 2 to AOI 1 shows significant difference across scenarios. These findings reinforce the need for detailed risk assessment classification based on emergency situation characteristics. The process of mentally processing and applying both retained and immediate information increases eye movement randomness, confirming the necessity for adequate adaptation time and training before task engagement.

Sungju Maeng, Makoto Itoh

Conference Proceedings

Digital assistant in Aviation: Monitoring, Understanding, and Supporting Operators

In aviation, actions and decisions must often be made rapidly, without ever compromising safety. With the growing advancement of artificial intelligence and human–machine teaming, digital assistants are increasingly being developed to support flight crews and air traffic controllers, potentially enabling operations with reduced crew sizes. In this paper, we reviewed a range of such systems designed for pilots and air traffic controllers, described their core functionalities, modes of interaction, and potential impact on human performance and safety. Their primary capabilities include supporting situation awareness, enhancing decision-making, managing cognitive load, regulating stress, and maintaining operator authority. Some systems also incorporate physiological monitoring to assess cognitive or emotional states and dynamically adjust automation levels to optimize performance and engagement.

Mickael Causse, Jean-paul Imbert, Alexandre Duchevet, Alexandre Veyrie, Christophe Hurter

Conference Proceedings

The application of microchannel heat sinks with triangle ribs for thermal management

A microchannel heat sink with a smooth and triangle-ribs embedded surface is examined using a simulation-based study. The heat sink is composed of microchannels, each of which is 1 cm long and 150 μm wide. The heat sink is studied using water. A range of Reynolds numbers, from 100 to 500, are used in the study. The thermal performance is computed using the fluids' surface temperatures as well as their inlet and exit temperatures. The analysis found that a greater Reynolds number increases the Nusselt number and the heat transfer coefficient. It has also been observed that the friction factor decreases as the Reynolds number increases. The heat transmission rate of the triangle-ribs microchannel was clearly higher than that of the smooth one. The total friction factor of the heat sink with triangle-rib microchannels is also found to be higher than that of the heat sink with smooth channels. Furthermore, it is noticed in the obtained results that as the Reynolds number rises, the pressure drop rises.

Fadi Alnaimat

Conference Proceedings

The issues related to agreement of software usage rules and its solution by UX approach

Before using the software, users are required to read and agree to the terms of use and the agreement regarding the handling of personal information. It is said that only about 5% of users read the consent form in its entirety. The most common reasons for this include: (1) It's pain, (2) The pros and cons are unclear, and (3) They all have the same content, so I've never had a problem not reading them.However, these terms and conditions are mainly agreements between the provider and the user, and include items to clarify where responsibility lies, so there are many problems caused by agreeing to the terms without fully understanding them. In order to avoid such troubles, according to a study, users need to understand the following four rules in these regulations. 1) Standards for account suspension, 2) Rules regarding money (payment methods, late fees, etc.), 3) Rules for withdrawal and cancellation, 4) Copyright rules.This time, we classified the contents of the terms of use for software and web applications and analyzed which of the four roots mentioned above they belong to. The subjects are: - General software - Online shopping sites - Auction sites The purpose of online shopping and auctions is to buy and sell things, so the focus is on operational aspects such as user qualifications, transactions, and exemptions rather than on terms for using regular software. In addition, the former is characterized by the fact that the operator and seller are almost the same, while the latter provides a place and the operator and seller are different. From this, we found that there are 3-4 items in terms that require special attention. The main purpose of the terms is an agreement between the provider and the user, and for this purpose, measures from the perspective of UX are necessary. Specifically, it is important to clearly show the "pre-use experience" in the UX concept. The aim is to create a UI that allows users to intuitively understand the four points mentioned above according to the principles of HCI. That is, -Display only the titles of items that require attention and the relevant items (items that have an impact) from the four rules according to the principles of 9241-112 (about 1-2 lines) and at the same time display an icon indicating the degree of danger -Hover the pointer over the icon to display a pop-up displaying the dangerous contentBy showing the concrete impact, such as conveying the image of an account being unusable in the case of an account suspension or conveying the actual damage that will occur in the case of financial matters, it is possible to make users understand the minimum terms and conditions.

Shinichi Fukuzumi

Conference Proceedings

OCR-based Quality Assessment and Auxiliary Review System for Semantic Information Extraction from Engineering Drawings

Optical Character Recognition (OCR) has been widely adopted to extract textual information from legacy engineering drawings, aiming to transform image-based PDF documents to semantically enriched digital models. However, the quality of drawings varies due to variations in sources and formats, which degrades the performance of OCR and lowers the accuracy of extraction results. Therefore, manual review is needed to correct OCR outputs, requiring additional time and labor. To address this issue, the authors proposed an OCR-based quality assessment method combined with an auxiliary review system to enhance both the accuracy and efficiency of textual information extraction. A set of semantic- and task-driven criteria was designed to evaluate drawing quality. A dataset of 50 bridge plans in PDF format was annotated with “high” or “low” quality labels, and the textual content was manually transcribed for OCR performance evaluation. The proposed method applied Tesseract OCR to extract textual information and automate the quality assessment process. Token-level confidence scores were computed, and drawings with an average score below 80 were classified as low-quality. In the auxiliary review system, tables detected were reconstructed, and cells with text below this confidence threshold were highlighted, enabling reviewers to focus on potentially error-prone regions. Experiments on the annotated dataset showed that the proposed method achieved a precision of 97.14% and a recall of 87.18% in classification. By excluding low-quality drawings, the precision increased by 17.84% and the recall increased by 18.96% in information extraction. Additionally, the auxiliary review system highlighted 36.81% of the cells, indicating a potential reduction of over 60% in manual review time. Overall, the proposed method provides a lightweight approach to improve OCR-based semantic information extraction from engineering drawings in terms of accuracy and review efficiency.

Bo Pang, Jiansong Zhang

Conference Proceedings

Out of the Mud: A user-centered AHP-based approach

Intertidal fishermen, primarily older workers, perform physically intensive tasks in highly variable environments characterized by tidal fluctuations and unstable mudflats (Hwang et al., 2023). The tools they rely on are often rudimentary and lack ergonomic considerations, offering little relief from physical strain. Furthermore, limited adaptability and learning capacity among these users make the development of specialized tools particularly challenging. This study identifies intertidal fishermen as the target user group, applies the Analytic Hierarchy Process (AHP) to systematically assess their needs, and develops user-centered design interventions aimed at reducing work-related fatigue and improving tool usability in real-world conditions. Recent ergonomic studies have shown that modular exoskeletons have significant stress reduction effects in industrial environments, effectively reducing back loads and improving operational efficiency (Qu et al., 2025; Xiang et al., 2024). However, research on intertidal fishermen as a labor group is still insufficient. As their labor force structure is aging, the physical challenges faced by this group in a highly dynamic and difficult-to-control natural environment require urgent attention (Wang et al., 2023; Park et al., 2022). In this context, the development of strongly adaptive, human-centered operational aids is becoming an urgent need to cope with the stress of physically intensive fishing work (Hwang et al., 2023).To support design decisions for the wearable support system, we used a hierarchical analysis (AHP, Saaty, 2008) to assess the importance of the elements in the product composition. The assessment was done by a focus group consisting of 12 fishermen (median age 58.4 years, standard deviation 6.7), 3 product designers and 3 fisheries experts, and a judgment matrix was constructed by two-by-two comparison, and the final weighting results obtained provided a scientific basis for prioritizing the functions of the system.Based on the outcomes of the AHP evaluation, the following prioritization of user needs was identified:AHP analysis revealed that physiological needs were most critical (weight = 0.5571), with lumbar support ranked highest. Operational needs (0.3202) followed, including lightweight construction, task assistance, and tool storage. Durability（0.1226）was identified as the most important psychological factor. These findings informed the prioritization of design features in the prototype.To balance performance and cost-efficiency, the final design prioritizes lumbar protection and integrates key features such as tool support and structural durability. Given the aging user population, the prototype maintains familiarity with existing products while emphasizing ease of use and adaptability. It features adjustable modular straps and pivot joints to accommodate long-handled tool operations. Field tests indicate notable improvements in back comfort, task efficiency, and tool handling compared to conventional equipment. This study presents a human-centered ergonomic solution tailored to the overlooked needs of intertidal fishermen. Grounded in AHP-based needs analysis and real-world testing, the design process resulted in a wearable system that improves physical comfort without disrupting traditional workflows. The findings contribute to a broader shift toward context-sensitive, user-informed design in labor-intensive sectors.

Enqi Ni, Hui Xie, Keqi Wang, Jiamin Fang, Ruochen Hu

Conference Proceedings

Enhancing Disaster Responses using Uncrewed Systems (UxS) as a Digital Twin (DT)

Conventional disaster response paradigms are fundamentally constrained by reliance on human-centric intelligence, which introduces significant cognitive and heuristic biases, resulting in sub-optimal decision-making and inefficient resource allocation under high-stress, dynamically evolving scenarios. This research posits a novel framework that transcends these limitations by operationalizing a cyber-physical System of Systems (SoS) architecture where a heterogeneous fleet of Uncrewed Systems (UxS) functions as a high-fidelity Digital Twin (DT). The cognitive core of this framework is a Model-Based Artificial Intelligence (MBAI) engine, a synergistic integration of Model-Based Systems Engineering (MBSE) methodologies with advanced AI. This MBAI leverages pre-compiled Pattern Libraries (PL), constraint-based mathematical models, and predictive physics-based and stochastic simulations (ModSim) to explore potential state-space evolutions and derive optimal control policies. The DT provides a real-time, synchronous emulation of the physical environment by assimilating multi-modal data streams from UxS sensor suites and disparate inter-agency systems. The system provides the functional mechanisms to enforce data consistency across a federated architecture, thereby adhering to the single source of truth principle, and meticulously documents data lineage to maintain a forensically sound chain of custody for all collected data objects. Architecturally, the complex UxS fabric is conceptualized using an SoS methodology and decomposed via the rigorous Systems Engineering (SE) Vee model to ensure robust integration, verification, and validation of all subsystems. Operationally, the MBAI-driven DT autonomously establishes comprehensive situational awareness, performs multi-objective optimization to orchestrate a minimum viable plan, and dynamically allocates assets to mitigate impacts in high-vulnerability sectors. The system is designed to interface directly with critical infrastructure nodes such as power, communication, and transportation to model interdependencies, predict and preempt cascading failures, and reinforce Emergency Support Functions (ESF). Initial field deployment of physical UxS assets focuses on high-resolution geospatial data acquisition to calibrate and validate the DT's underlying models. The longitudinal, high-fidelity data generated throughout the response lifecycle (from heroic to disillusionment phases) is invaluable for post-hoc forensic analysis, model refinement, and provides a quantitative, auditable basis for Federal Emergency Management Agency (FEMA) assessments and insurance claims adjudication, ensuring verifiable data provenance and maintaining a strict chain of custody for evidentiary purposes. This extensible framework is agnostic to disaster typology and is engineered to enhance operational resiliency across a spectrum of catastrophic events, from hydrometeorological and geophysical events to technological and anthropogenic crises.

Sai Raghava Pathuri, Ninad Pandit, Bhushan Lohar, Sudhanshu Tarale

Conference Proceedings

Human-AI Co-Creation: A Framework for Collaborative Design in Intelligent Systems

As artificial intelligence (AI) continues to evolve from a back-end computational tool into an interactive, generative collaborator, its integration into early-stage design processes demands a rethinking of traditional workflows in human-centered design. This paper explores the emergent paradigm of human-AI co-creation, where AI is not merely used for automation or efficiency gains, but actively participates in ideation, visual conceptualization, and decision-making. Specifically, we investigate the use of large language models (LLMs) like GPT-4 and multimodal diffusion models such as Stable Diffusion as creative agents that engage designers in iterative cycles of proposal, critique, and revision.Our study is grounded in a mixed-methods experimental setup involving 24 professional and novice designers from diverse backgrounds. Each participant completed two design tasks: one using a conventional digital toolset (Adobe XD, Figma, Sketch), and another with access to AI-assisted tools that provided both text-based concept ideation and image generation support. We captured all interaction data, output artifacts, and post-task interviews to understand how AI affects cognitive load, ideation fluency, and perceived creativity. The AI models were prompted using open-ended and task-specific queries, and designers could iterate on or reject outputs at will.The findings reveal several key patterns. First, AI significantly reduces the time spent in the “blank slate” phase of ideation, providing a scaffold of initial concepts that users can build upon or remix. Second, the outputs generated by AI often diverge from conventional aesthetics or functional patterns, serving as “creative dissonance” that pushes designers toward new conceptual territories. Third, participants reported a stronger sense of cognitive partnership with AI when systems provided rationale for their suggestions, suggesting that explainability is critical for trust and effective collaboration.We introduce a co-design framework that includes three levels of AI involvement: passive assistance (suggestive prompts), interactive co-creation (real-time response and refinement), and proactive collaboration (AI initiating alternative design pathways). Furthermore, we discuss the ethical and cognitive implications of relying on AI for generative input, including issues related to bias, originality, and designer agency. Our work contributes both to design theory and practical system development, providing guidelines for building next-generation design platforms that are AI-native and human-centered.In conclusion, the integration of generative AI into the design process has the potential to augment not just efficiency but also originality, inclusion, and resilience of design outputs. However, successful implementation requires a redefinition of authorship, transparency in AI behavior, and mechanisms for human oversight and reflection. This paper sets a foundation for future work in human-AI design partnerships and proposes concrete methodologies for evaluating and scaling such systems across design disciplines.

Zhangqi Liu

Conference Proceedings

Multi-scale Feature Fusion Enhanced Lightweight Detection

With the advancement of automation technology, automated car wash systems have been widely utilized. However, Such technology consume large amounts of Water resources. Moreover, existing approaches face the challenge of standardized parameter settings, which make it difficult to adapt to variations in vehicle body structures and surface scratch features, thereby hindering effective stain removal while protecting the car body. Existing approaches focus on the investigation and development of car wash machines. They employ conventional image processing techniques such as threshold segmentation and mechanical water recycling optimization strategies to achieve improved cleaning efficiency and enhanced defect detection and cleaning precision. However, these traditional, statistics-based approaches face significant challenges in handling complex real-world conditions. They exhibit suboptimal detection accuracy, insufficient precision in dynamic water flow control, and often suffer from a lack of training datasets, while traditional algorithms struggle to balance precision with real-time constraints. These limitations underscore the need for advanced intelligent detection and control methods.To tackle these challenges, a vehicle defect dataset comprising 10,320 samples across 11 categories was constructed. Then, an optimized car wash model based on the RetNet architecture was developed, integrating a dynamic channel attention mechanism (DCAM) and a multi-scale feature fusion module to enhance the model's adaptability to complex environments. The model was subsequently trained and evaluated through simulation, demonstrating notable improvements in defect detection accuracy and cleaning efficiency. The contribution of this paper are as follows:1）A dedicated vehicle body defect dataset comprising 10,320 samples was constructed for model development and training, thereby alleviating the data scarcity issue in the intelligent cleaning domain.2）A lightweight detection algorithm based on the RetNet architecture was designed, incorporating a dynamic channel attention module and a multi-scale feature fusion mechanism to enhance both defect recognition accuracy and processing efficiency.3）A multi-round iterative optimization framework was implemented, integrating knowledge distillation and a hybrid loss function to systematically improve the model’s lightweight capability and small-target detection performance, ultimately achieving a synergistic optimization of cleaning strategies and resource utilization.Experimental results shows that the lightweight algorithm based on the improved RetNet achieves a Precision of 88%, a Recall of 87%, an Accuracy of 88%, and an F - Score of 87% on the self - built vehicle body defect dataset (10k - 12k images). Compared with the optimal baseline algorithm, it has improvements of 1.8%, 1.8%, 3.4%, and 2.6% respectively. Moreover, the generalization experiment results on the CIFAR - 10, STL - 10, ImageNet, and ObjectNet datasets demonstrate that the proposed scheme has good robustness. The experimental results demonstrate that our proposed RetNet optimization algorithm achieves robust performance across several evaluation metrics. On the CIFAR-10 dataset, the Precision, Accuracy, Recall, and F-score are 89%, 89%, 88%, and 89%, respectively. Similarly, on STL-10, these values are consistently 87%, while on ImageNet they reach 88%, 88%, 87%, and 88%. In the case of the ObjectNet dataset, the algorithm attains scores of 86%, 86%, 89%, and 87% for the four respective metrics. These findings indicate that the RetNet optimization algorithm can effectively solve the problem of detecting scratches on vehicle bodies.

Peiyan Zhong, Jiazheng Zhu

Conference Proceedings

Detecting Ambiguity Aversion in Cyberattack Behavior to Inform Cognitive Defense Strategies

Adversaries (hackers) attempting to infiltrate networks frequently face uncertainty in their operational environments. This research explores the ability to model and detect when they exhibit ambiguity aversion, a cognitive bias reflecting a preference for known (versus unknown) probabilities. We introduce a novel methodological framework that (1) leverages rich, multi-modal data from human-subjects red-team experiments, (2) employs a large language model (LLM) pipeline to parse unstructured logs into MITRE ATT&CK-mapped action sequences, and (3) applies a new computational model to infer an attacker’s ambiguity aversion level in near-real time. By operationalizing this cognitive trait, our work provides a foundational component for developing adaptive cognitive defense strategies.

Stephan Carney, Soham Hans, Sofia Hirschmann, Stacy Marsella, Yvonne Fonken, Peggy Wu, Nikolos Gurney

Conference Proceedings

A Framework for Aligning Cybersecurity and Business Strategy - From Cost to Investment

In recent years, the situation surrounding cyberattacks has continued to grow increasingly sophisticated and cunning. Amidst this situation, companies, particularly operating businesses, need to advance their countermeasures against cyberattacks. However, it is difficult to say that cybersecurity measures are necessarily well-established. On the other hand, a survey on the actual state of information security measures among small and medium-sized enterprises (SMEs), published by the Information-technology Promotion Agency (IPA), an external organization of the Ministry of Economy, Trade and Industry (METI) which oversees Japan's information security sector, also reports that implementing countermeasures has reduced the damage from cyberattacks. Furthermore, due to additional regulations and heightened security awareness among client companies, security measures are increasingly being demanded by business partners. In this environment, companies must develop medium- to long-term security strategies, rather than focusing solely on short-term costs.In this paper, we analyze why companies struggle to advance security measures, examining the causes of the gap between business strategy and security strategy, and proposes solutions. The gap analysis references the Balanced Scorecard (BSC) and is conducted across four perspectives: financial, customer, internal processes, and people. It analyzes the causes within each category and suggests countermeasures. Furthermore, in this paper, we implement one countermeasure: creating a “Security Scorecard” that maps cybersecurity measures based on the BSC.

Hiroyuki Hasegawa, Kenji Watanabe, Ichiro Koshijima, Masahiro Arakawa

Conference Proceedings

Balancing Agility, Operational Business Requirements and Cybersecurity in a Large Public Organization

Enterprise cybersecurity is undergoing significant transformation due to the widespread adoption of agile development and self-steering teams, particularly in large organizations. Traditionally, cybersecurity governance has been centralized, relying on structured coordination across people, processes, technologies, and compliance mechanisms. However, agile methodologies—marked by decentralized, autonomous teams—have shifted organizational dynamics from hierarchical to distributed models. While this enhances responsiveness and innovation, it also introduces fragmentation in cybersecurity responsibilities, complicating unified decision-making and the enforcement of security controls.This study explores how large agile enterprises can effectively manage cybersecurity, with a specific focus on ransomware threats. Through qualitative interviews with nine cybersecurity professionals from a highly digitalized public organization in the Netherlands, the research identifies two core organizational tensions: (1) balancing agility and cyber security in decision making about security controls and risk management, and (2) balancing operational business requirements with cybersecurity improvement.The first tension stems from the fragmentation of cybersecurity responsibilities across agile teams. Respondents reported weakened accountability, inconsistent policy enforcement, and an over-reliance on tools to bridge communication gaps. These challenges are linked to mechanistic thinking—an outdated organizational mindset that views departments as isolated units. This leads to siloed operations and a narrow focus on technical solutions. To address this, the study advocates for a systems thinking approach, which views organizations as dynamic networks of interdependent elements. Systems thinking emphasizes holistic understanding, collaboration, and feedback loops.A key recommendation is the introduction of boundary spanners—individuals who bridge communication gaps between teams and align local actions with enterprise cybersecurity goals. These roles facilitate cross-team coordination, support unified decision-making, and help integrate cybersecurity efforts across the organization.The second tension involves the misalignment between the steady rhythm of operational teams and the dynamic pace of cybersecurity innovation. Operational teams prioritize stability, while innovation efforts require flexibility and rapid iteration. This mismatch is exacerbated by Out-Group Bias, where teams resist adopting solutions developed externally, leading to inconsistent security practices and delayed implementation of improvements.To overcome these challenges, the study proposes a programmatic approach to cybersecurity improvement. A program, defined as a coordinated set of related projects, ensures strategic alignment, resource allocation, and effective decision-making. The approach incorporates short-cycled project phases—explore, experiment, pilot, and scale—each with clear objectives and standardized methods. This structure accommodates operational constraints while ensuring timely progress and shared ownership.A successful example of this method is found in the Dutch financial sector, where the Partnership for Cyber Security Innovation (PCSI) implemented a four-month cycle with joint steering committees. This setup promoted inclusivity, countered Out-Group Bias, and enhanced cross-organizational cybersecurity awareness.In conclusion, the study underscores the need for systemic thinking and structured program management to align agile practices with robust cybersecurity strategies. By addressing internal tensions and fostering collaboration across teams, organizations can enhance their resilience against complex cyber threats while maintaining the benefits of agility. Future work will involve field-testing these models, including training boundary spanners and implementing short-cycled programs, with a one-year implementation horizon recommended for optimal impact.

Mascha Van Dort, Rick van der Kleij

Conference Proceedings

Coordinating Asset Owner and PSIRT for CRA Vulnerability Recognition: Evidence-Based Mechanisms from Coordination Theory

The EU Cyber Resilience Act (CRA) requires manufacturers to provide early warning within 24 hours, detailed notification within 72 hours, and final reporting within 14 days after corrective measures become available, upon becoming aware of actively exploited vulnerabilities (Article 14). However, the evidence necessary to establish awareness exists primarily in asset owner environments, and asset owners bear no reporting obligation. This creates a structural coordination challenge: manufacturers require evidence they cannot independently access, and fixed reporting deadlines commence upon awareness. This study applies Malone & Crowston's coordination theory to identify three dependency relationships: bidirectional knowledge asymmetry (producer-consumer relationship) between asset owners who hold evidence and PSIRTs who hold product knowledge; time allocation (shared resource) under fixed reporting deadlines (24h/72h/14d); and misalignment between different objectives (task-subtask dependency). We propose a three-layer mechanism for managing these dependencies. C0 (Reachability) provides reporting channels. C1 (Evidence Coordination Profile) decomposes Article 3(42) awareness definition into five propositions and structures evidence into four categories (E1-E4), enabling the establishment of awareness and phased reporting. C2 (Incentive Design) converts asset owners’ voluntary cooperation into organizational security improvement through three benefits. These three mechanisms mutually reinforce each other to achieve continuous coordination. Theoretically, this extends the coordination theory to regulatory compliance contexts in which coordination is voluntary. Practically, it provides implementable guidance for manufacturers facing CRA enforcement by 2027.

Jumpei Tahara, Kenji Watanabe

Conference Proceedings

Securing resilient maritime logistics: Seaport threat analysis

Maritime logistics form the backbone of global trade by handling approximately 90% of worldwide commerce by volume. Since global supply chains grow more interconnected and demand for just-in-time delivery increases, the resilience and robustness of maritime logistic systems have become more critical than ever. Today digital systems manage every aspect of the logistic operations. Therefore, protecting maritime logistics from cyber threats is essential to guarantee flow of goods globally. From port infrastructure to shipping route stability, the ability to maintain efficient logistic operations in the face of digital systems disruptions is essential for economic stability and growth of many countries. Maritime sector evolves due to development of new digital technologies. This offers new attack opportunities for hackers that are too often protected by rogue nations. Therefore, robust maritime logistic system that integrates well with on road and railway transportation is no longer a luxury but a necessity. Building such systems requires investment, innovation, and international collaboration. The future of maritime logistics depends not only on how fast and far goods move, but how reliably and securely they do so under any condition. Therefore, it is important to fully understand what are potential threats for global maritime logistics. A robust maritime logistic digital system is one that can anticipate, absorb, adapt, and recover from disruptions, whether they are caused by natural disasters, cyberattacks, geopolitical tensions, pandemics, or economic shocks. Robustness is not about avoiding disruption entirely but about minimizing impact and ensuring rapid recovery. Key pillars of robustness include understanding real-time operational status, maintain adequate backup systems, plan and utilize available resources dynamically, conduct risk assessment and plan crisis response. Resilience is not solely a maritime issue. It depends heavily on how well sea transport integrates with land logistics such as rail and road transportation. Therefore, robust maritime systems require coordinated infrastructure development with land based logistic operators that utilize latest technologies such as automated driving and autonomous vehicle fleet management. No nation can ensure maritime resilience alone. Global supply chains demand cross-border cooperation and policy alignment. This paper analyses potential threats for maritime logistic systems in seaports that are critical focal points for goods enroute to final customers.

Markus Sihvonen

Conference Proceedings

Cybersecurity Standards in Critical Infrastructure Protection: A Maturity Model for Finnish SMEs

The protection of critical infrastructure such as energy grids, water supply systems, and transportation networks has become a central concern in national and organizational security strategies. These systems form the backbone of societal functionality, and disruptions can lead to severe economic losses, safety risks, and societal instability. As digitalization accelerates, their vulnerability to cyber threats increases, making cybersecurity standards essential for both operational resilience and strategic preparedness. This study investigates whether Finnish companies utilize cybersecurity standards such as ISO/IEC 27001 and the NIST Cybersecurity Framework to safeguard critical infrastructure, and how their adoption influences strategic decision-making, operational practices, competence development, and stakeholder collaboration. These standards support regulatory compliance and unify practices across sectors, but their effective implementation requires leadership commitment, resources, and continuous development, especially in environments where regulation may lag technological change. The findings show that standards are widely adopted, but the extent and effectiveness vary significantly depending on organizational size, industry, and cybersecurity maturity. Larger organizations tend to integrate standards into strategic decision-making and risk management, whereas smaller firms often apply them reactively. The effectiveness of standards is highest when combined with continuous improvement, maturity assessments, and targeted training. Cybersecurity standards are not merely technical guidelines but strategic tools for leadership, planning, and culture-building. To enhance their impact, companies should integrate standards into business strategy and governance, invest in staff training and competence development, leverage expert networks and collaborative partnerships, and actively engage stakeholders, especially in sectors where cybersecurity directly affects operational continuity. This research provides actionable insights for companies, policymakers, and security professionals aiming to improve national resilience through standardized and proactive cybersecurity practices.

Kim Rejman, Markus Sihvonen

Conference Proceedings

Risk-Based Model for OT Security Technology Implementation and Segmentation

As digital connectivity expands across factory systems, cybersecurity risks within industrial supply chains have grown significantly. Previous research by the authors addressed these challenges by developing a web-based risk assessment tool, analyzing responses from 225 factory sites, identifying governance issues, and introducing OT (Operational Technology) risk workshops to support cybersecurity posture visualization.Building on this framework, this study focuses on the "Technology" layer of OT cybersecurity. Rather than applying conventional IT security measures to OT environments, a risk-based approach is proposed to guide technology selection. This approach introduces a two-axis framework: (1) threat detection capability (known vs. unknown) and (2) automation in incident response. These axes produce four models: X (manual response to known threats), X+ (automated response to known threats), Y (manual response to unknown threats), and Y+ (automated response to unknown threats).Each model is mapped to real-world security solutions such as antivirus tools, Unified Threat Management (UTM) systems, OT-IDS (OT Intrusion Detection Systems), and application whitelisting. For example, USB-based antivirus tools align with Model X, UTM systems fit X+, and behavioral analysis or whitelisting tools relate to Y or Y+, though the latter are more complex to implement due to operational risks.The study also highlights the role of logical network segmentation in reducing cyber risk. A sample configuration divides the factory into zones (e.g., production control, parts management, DX promotion), each with different risk profiles. Without segmentation, malware can easily spread across zones, increasing downtime and recovery cost. Segmentation, paired with the X/Y classification, allows tailored security strategies that improve cost-effectiveness and align with business risk.This framework reframes technology selection as a risk mitigation strategy, supporting investment decisions and stakeholder alignment. It also complements earlier governance-oriented work by connecting technology choices with risk workshop outcomes.Future research should explore industry-specific applications. For instance, SMEs may start with X or X+ models, while industries with low downtime tolerance (e.g., automotive, food) may pursue X+ or Y+. In highly sensitive operations (e.g., blast furnaces), Y+ may be required. Digital exposure levels should also be considered.This study offers a structured, scalable method for selecting OT security technologies, enabling practical deployment in resource-constrained environments while maintaining flexibility and risk awareness.

Hiroshi Sasaki, Kenji Watanabe

Conference Proceedings

Engaging K-12 Students with Real-World Experiences from Cybersecurity Professionals

Cybersecurity is a topic of critical importance in our world today, and it is increasingly important for middle school and high school students to learn the concepts of cybersecurity. At this point in time students are not required to take a course in cybersecurity, although it is available as an elective in some schools. They often are only given bits and pieces of information about cybersecurity and personal safety at school. Thus, summer camps focused on cybersecurity are a great way for students to learn more about this topic. In summer camps at Texas A&M University, students learned about cybersecurity through experiential learning in safe environments, games, lectures, and presentations from university faculty and cybersecurity professionals. In this paper, we focus on the presentations from cybersecurity professionals from various university departments and from outside the university. We identify six cybersecurity concepts addressed - availability, keeping it simple, defense in depth, confidentiality, thinking like an adversary, and integrity. We describe some of the presentations that students heard that illustrated these concepts and how they applied to students' lives. Some of the presentation topics we discuss center around safe online behavior, social engineering, and cyber attacks. In addition, the increasing need for cybersecurity professionals in many different fields was shared with students, along with several different pathways to take towards those careers. We provide the results from students' daily reflections about their learning from these presentations. The results include frequencies from multiple-choice questions about the level of learning they gained from the presentations and free response questions for which they chose an activity or presentation from the day as their greatest learning opportunity of the day and explained what they learned. Finally, we discuss how the camp experience and knowledge gained by students is an important part of their learning in regard to cybersecurity, with suggestions about carrying on the work of educating secondary students about cybersecurity is important in decisions they make regularly in their lives.

Sandra Nite, Wesley Brashear, Halle Gray, Seonhu Lee, Dhruva Chakravorty

Conference Proceedings

Empirical Study of Information Sharing and Decision-Making in IT/OT Incident Response

In recent years, cyberattacks have grown increasingly advanced and sophisticated, requiring organizations to build comprehensive defenses that extend beyond technical controls to include human factors. For critical infrastructure facing IT/OT convergence risks, the establishment of rapid and accurate information-sharing mechanisms is central to strengthening resilience. As part of a human-centered approach, this study designed and conducted a Tabletop Exercise (TTX) with 119 frontline and managerial participants from Japanese critical-infrastructure firms. Using communication logs recorded during the exercise, the analysis examined actual information flows and assessed how differences in security-education levels affect command structures and network dynamics. The results indicate that more advanced education promotes the formation of flexible network structures and can support faster, more autonomous judgment at the operational edge.These findings offer actionable guidance for improving TTX design, concretizing information-sharing protocols, and standardizing incident-response procedures, thereby contributing to enhanced organizational security preparedness and resilience.

Kenta Nakayama, Ichiro Koshijima, Kenji Watanabe

Conference Proceedings

Security Logs to ATT&CK Insights: Leveraging LLMs for High-Level Threat Understanding and Cognitive Trait Inference

Understanding adversarial behavior in cybersecurity has traditionally relied on high-level intelligence reports and manual interpretation of attack chains. However, real-time defense requires the ability to infer attacker intent and cognitive strategy directly from low-level system telemetry such as intrusion detection system (IDS) logs. In this paper, we propose a novel framework that leverages large language models (LLMs) to analyze Suricata IDS logs and infer attacker actions in terms of MITRE ATT&CK techniques. Our approach is grounded in the hypothesis that attacker behavior reflects underlying cognitive biases such as loss aversion, risk tolerance, or goal persistence that can be extracted and modeled through careful observation of log sequences. This lays the groundwork for future work on behaviorally adaptive cyber defense and cognitive trait inference. We develop a strategy-driven prompt system to segment large amounts of network logs data into distinct behavioral phases in a highly efficient manner, enabling the LLM to associate each phase with likely techniques and underlying cognitive motives. By mapping network-layer events to high-level attacker strategies, our method reveals how behavioral signals such as tool switching, protocol transitions, or pivot patterns correspond to psychologically meaningful decision points. The results demonstrate that LLMs can bridge the semantic gap between packet-level logs and strategic intent, offering a pathway toward cognitive-adaptive cyber defense.

Soham Hans, Stacy Marsella, Sofia Hirschmann, Nikolos Gurney

Conference Proceedings

Team Cybersecurity Training: A Feasibility Study

To maintain the critical functioning of the United States’ computing infrastructure, a virtual-simulation range for cybersecurity training has been established to train cybersecurity teams. The present study’s objective is to review the feasibility of this training range, using the approach of a self-report survey collected from trainees. Results show the usefulness of the range, while also revealing paths for improvement. Eighty-two cybersecurity professionals replied to a survey comprised of Likert items and open responses. Results from the Likert items showed positive signs of the training’s usefulness. User’s confidence in managing cyberthreats mainly increased or remained unchanged after training. Individuals mostly reported their teams accomplishing the scenario task without much confusion. For open responses, the most-liked aspects of training were its challenge, its realism, and the involvement of teamwork. Next steps are to improve the training range and extend research directions. Based on results, range improvements are to integrate relevant ethical scenarios, add new tools, lengthen the scenario, and give refresher training. Results of strong task cohesion and high collective orientation suggest issues with technical factors. Other next steps are to use the range to improve the personnel selection of cybersecurity professionals, and to capture performance and subjective perceptions repeatedly at team levels, while considering team age, size, and composition.

Jonathan Hurter, Crystal Maraj, Bruce Caulkins, Corey Wrenn

Conference Proceedings

Risk Psychology and Cyber Attack Tactics

Cybersecurity breaches are increasing in frequency and complexity, emphasizing the need to comprehend technical vulnerabilities and attacker behavior. This study examines the impact of individual cognitive traits and experimental framing on the selection of cyber techniques in a controlled virtual environment. Drawing on data from two treatment conditions (ADMC vs. control), we examined behavioral outcomes in 64 participants using psychometric assessments, skill-based performance metrics, and in-scenario technique choices mapped to the MITRE ATT&CK framework. We used both multinomial logistic regression and UCLA-style binary logistic models to test whether individual differences—such as open-mindedness (GRIPS), cognitive reflection (CRT), and resistance to framing (ADMC_RC2)—as well as treatment group, predicted the likelihood of choosing specific reconnaissance and attack techniques. In these models, Initial Access was used as the base technique for comparison. While multinomial models provided insights into relative strategy preferences across multiple categories, the binary UCLA models revealed more detailed effects of individual traits on the likelihood of choosing key techniques such as Lateral Movement or Collection. Preliminary results indicate that individual predictors are overall weak, but early trends suggest that framing conditions may interact with trait profiles to influence technique diversity and decision-making. These findings contribute to the growing intersection of cybersecurity and behavioral science, with implications for attacker modeling, adaptive defense, and mitigating cognitive vulnerability.

Rubens Kim, Stephan Carney, Soham Hans, Sofia Hirschmann, Stacy Marsella, Peggy Wu, Yvonne Fonken, Nikolos Gurney

Conference Proceedings

Machine Learning-Based Analysis of Fatal Construction Accidents Using SHAP: Insights for Safety-Assistive Vehicle Applications

The construction industry is among the most hazardous sectors, with frequent serious injuries and fatalities. This study investigates the key factors contributing to fatal accidents and explores how safety-assistive vehicles—currently limited to basic alarm and control functions—can be advanced into comprehensive safety management tools. Utilizing Korea's national accident database (CSI) from 2019 to 2023, we analyzed 15,807 cases, including 807 fatal incidents (5.1%). Predictive models employing CatBoost and AdaBoost yielded strong performance (AUC: CatBoost 0.912; AdaBoost 0.908). SHAP analysis identified top predictors of fatality: falls, worker negligence, hazardous objects, small-scale sites (<20 workers), and high-value projects (>$76.9M). Our results indicate that integrating predictive analytics may enable safety-assistive vehicles to go beyond alarms, facilitating real-time detection of accident risks, hazardous zones, and unsafe behaviors. This proactive capability can enhance safety management at construction sites. The study demonstrates the practical utility of machine learning for identifying high-risk conditions and guiding the development of smarter safety-assistive systems. Future research will focus on applying computer vision and detection technologies to further improve real-time accuracy.

Bom Yun, Jongil Yoon, Joonsoo Bae

Conference Proceedings

To Rotate or Not to Rotate – How Does Job Rotation Impact Musculoskeletal Disorder Risk?

To address work-related musculoskeletal disorders (WMSDs; e.g., carpal tunnel syndrome), some companies utilize job rotation, systematically rotating workers through different jobs within a workday. Prior research suggests that while job rotation may decrease WMSD risk for some workers, other workers may see their risk increase. There is a lack of evidence from actual worksites that utilize job rotation on the impact on WMSD risk for all workers involved in job rotation. The objective was to compare WMSD risk for job rotation schemes to jobs not involved in job rotation. Workers from U.S. manufacturing companies involved in rotation schemes with four jobs (N=42) that rotate within a workday were evaluated for WMSD risk factors for the hand/wrist, shoulder and low back at each job. WMSD risk was determined for each worker and each rotation scheme utilizing validated fatigue failure tools (DUET for hand/wrist, LiFFT for low back, The Shoulder Tool (TST) for shoulders) and estimated WMSD risk for each job in the rotation scheme assuming the job was performed for the whole workday (not involved in rotation). Job rotation resulted in increases in DUET risk probabilities of approximately 10% compared to not rotating for the right (p<0.0002) and left (p<0.0002) hand/wrist, and an approximate 6% increase in LiFFT and TST risk probabilities compared to not rotating (LiFFT: p<0.0002; right shoulder: p=0.0004; left shoulder: p=0.0028). Job rotation also resulted in 3 or 4 of the jobs in rotation schemes increasing in risk for 67% of the schemes for DUET, 43% of the schemes for LiFFT, and approximately 50% of the schemes for TST. The results demonstrated job rotation resulted in modest (6% to 10%) but significant increases in WMSD risk across multiple body regions compared to working in jobs that don’t rotate. Additionally, rotating workers through four-job rotation schemes resulted in the majority of jobs in these schemes increasing upper extremity risk compared to not rotating. The decision to rotate or not may in part depend on the WMSD risk levels for jobs being considered for inclusion in the rotation schemes. Inclusion of jobs that have elevated risk (e.g., moderate or high risk) may decrease the risk for those workers who otherwise would work the full workday in those jobs, but will likely increase the risk to workers in jobs that have lower risk.

Michael Jorgensen, Kermit Davis, Sean Gallagher, Susan Kotowski, Aditya Ahire, Mostafa Taheri, Hai Nguyen, Dickson Rungere, Amour Dondi, Ryan Bellacov, Mercy Omoifo-irefo

Conference Proceedings

Educational Interventions for Rural Maternity Care: Provider Perspectives on Patient Education Materials and Communication Strategies

Effective patient education is critical for improving maternity health outcomes, particularly in rural areas where access to care is limited. This study examines healthcare providers' perspectives on current educational tools and strategies for maternity care in upstate rural New York. We conducted a semi-structured focus group with 13 healthcare professionals (i.e., directors, professors/researchers, clinical managers/leaders, and associates) in rural New York to understand the educational needs, barriers, and opportunities for improving patient education in rural maternity care. The focus group was conducted via Zoom, and the entire session was recorded and subsequently transcribed using the Otter.ai transcription tool. Two independent reviewers analyzed the transcripts, and any discrepancies were resolved through consensus. The thematic analysis approach used was deductive, and guided emergent themes aligned to the categories of CONTENT, FORMAT, and DELIVERY MODE. Our analysis revealed significant patient education and communication challenges associated with traditional educational approaches, highlighting the need for more patient-centered and tailored educational materials. Overall, the findings underscore the need for greater support for rural maternal care leaders and practitioners to deliver more contextually and culturally appropriate, patient-centered educational materials in rural settings. Implementing targeted interventions that provide accurate, applicable, easily-accessible, inclusive, and user-friendly digital educational resources on maternal care is essential to effectively support this patient population.

Kimberly Harry, Safa Elkefi

Conference Proceedings

Building Blocks for Effective Modeling and Simulation of Systems Lifecycle using an End-to-End Model-Based Systems Engineering Framework

Model-Based Systems Engineering (MBSE) has become a cornerstone of modern Systems Engineering (SE), enabling complex system design, analysis, and lifecycle management through formalized modeling methodologies. The Systems Modeling Language (SysML), as the de facto modeling standard, facilitates system representation but faces challenges in extensibility and adaptability across diverse domains. Existing MBSE frameworks often lack adaptability, constraining cross-domain application, hindering model reuse in industry applications in addition to lack of interfaces from the left side to the right side, resulting in various modelling gaps. This paper presents the conceptual design of an innovative “End-to-End Model-Based Systems Engineering (E2E-MBSE) Framework”, which addresses the persistent challenges in applying existing frameworks across the full system lifecycle. While MBSE is a promising alternative to traditional document-based approaches, its industrial application remains limited by the rigidity and domain-specific nature of current frameworks like DoDAF and TOGAF, which often fail to provide a cohesive approach from a system's inception to its disposal. The proposed E2E-MBSE Framework is architected as a next-generation solution, designed to overcome limitations in Industry 5.0 by offering a structured, model-centric approach to address core challenges like integrating human-centric design, empathic design, promoting sustainability, and enhancing resilience. The framework's core innovation is a modular, pattern-based approach that utilizes a library of reusable and scalable SysML packages. This structure provides a customizable "toolkit" rather than a rigid, monolithic standard, allowing practitioners and researchers to tailor the methodology to specific industry applications or domain-specific needs. Crucially, the framework is predicated on the foundational capabilities of a standalone architecture, with formal semantics and integrated APIs integration providing a technical backbone for developing a "Total System Model" (TSM) that serves as a single source of truth and evolves throughout the system's entire lifespan. This approach fundamentally redefines the relationship with the systems engineering Vee model. Instead of treating Verification and Validation (V&V) as discrete, late-stage activities on the right side of the V, the framework leverages real-time traceability capabilities to embed automated, continuous V&V directly within the modeling process. This creates a living "digital thread" that ensures traceability and consistency from requirements decomposition to system retirement, thereby significantly reducing project risk and enhancing operational efficiency. The research outlines the framework's architecture, its foundational principles, and its potential to revolutionize systems engineering by providing a flexible, scalable, and verifiable methodology for the complex systems of the future.

Joshua Adelabu, Bhushan Lohar, John T Wade, Sean Walker, Carlos Montalvo

Conference Proceedings

Knowledge structuring for initial response to the 2024 Noto Peninsula Earthquake in Japan

Large-scale disasters impose an immense burden on affected municipal staff, as they are tasked with duties far exceeding their regular responsibilities, such as managing shelters and distributing aid supplies. While support personnel from other　municipalities are crucial in these situations, the valuable knowledge and experience they gain on-site are often not systematically retained by the supported municipality after their departure. This research addresses this critical gap by aiming to systematically organize and visually share the comprehensive disaster response experiences of dispatched personnel, including the challenges they faced and the solutions they implemented, thereby ensuring that this vital information remains with the affected municipalities for future use.Previous research has explored various aspects of disaster management, including knowledge accumulation from past events and the optimization of municipal staffing. However, there is a lack of research focused on structuring this critical knowledge in a format that can be used quickly and intuitively at all stages of a disaster response. Our study fills this void by proposing a novel system that structures knowledge into a mandala format, facilitating quick comprehension and informed decision-making. The mandala format, originally a symbolic diagram in Buddhist cosmology, has recently been adapted as a tool for idea generation and information organization in fields like education and business.Specifically, this research introduces the "Mandala" concept, focusing on the critical issue of delayed initial responses during disasters. The Mandala systematically organizes and visualizes the causes of these delays, which are derived directly from the experiences of dispatched personnel, thereby providing a clear roadmap for preventing similar issues in future events.To construct Mandala, we employed a hybrid analytical approach, combining both inductive and deductive reasoning. We utilized interview data from the 2024 Noto Peninsula Earthquake in Japan as our primary source. The methodology involved initial open coding to decontextualize the interview data, followed by thematic classification. The final step was the construction of the Mandala by reconfiguring the thematic classification results.Our analysis highlighted the profound importance of knowledge held by dispatched support personnel for streamlining operations during the initial stages of a disaster, especially considering the overwhelming demands on local municipal staff. Through this process, we successfully constructed a Mandala with "Causes for Delayed Initial Response" at its center. This Mandala effectively organizes multiple contributing factors and their interrelationships, systematically derived from the interview data. We anticipate that this Mandala will serve as an invaluable tool for municipal staff, enabling swift and effective decision-making during future disaster responses.In conclusion, this study was able to demonstrate the effectiveness of a hybrid qualitative data analysis approach in structuring knowledge from published text data into a visually intuitive mandala format. This research is expected to make a significant contribution to a more efficient, effective, and resilient initial response capacity in the event of future disasters, ultimately minimizing the impact on the community.

Katsuhito Fukuta, Youji Kohda, Hideomi Gokon

Conference Proceedings

Effects of shared leadership and absorptive capacity on software development agility: a preliminary study

In modern software development, teams face numerous challenges, particularly frequent and diverse changes in requirements. The ability to respond promptly and effectively to such changes depends not only on technical expertise but also on human and team factors—specifically, how members learn from one another and leverage their diverse backgrounds. To succeed, teams must cultivate software development agility (SDA) supported by effective leadership capable of guiding them through shifting requirements. Grounded in dynamic capability theory, this study investigates the relationship between shared leadership and absorptive capacity and their combined influence on software development agility. The proposed theoretical model illustrates how shared leadership enhances a team’s absorptive capacity, thereby fostering SDA. This work contributes to the human-centered software development literature by offering new insights into team dynamics within the context of requirement changes, viewed through the lens of dynamic capability theory.

Szu Yu Chen, Jung-chieh Lee, Chung-Yang Chen, Pei-Chi Chen

Conference Proceedings

How generative AI is reshaping UI/UX design workflows: A systematic review

As GenAI technologies such as large language models, diffusion models, and multimodal generative systems increasingly permeate design workflows, their implications for creativity, methodology, ethics, and collaboration demand critical scholarly attention. This paper presents a systematic literature review of generative artificial intelligence (GenAI) in user interface (UI) and user experience (UX) design, drawing on fifty peer-reviewed and preprint articles published between 2020 and 2025. The review is structured around five research questions, addressing: (1) the stages of the UI/UX design process where GenAI tools are most actively applied, (2) the methodological approaches used to evaluate their integration, (3) the ethical considerations arising from their use, (4) models of human-AI collaboration in design practice, and (5) the research gaps that shape the future trajectory of this field. Findings indicate that while GenAI tools are widely adopted in prototyping and visual asset generation, their use in early-stage conceptualization and UX evaluation remains limited. The literature also reveals methodological fragmentation and a lack of standardized evaluation frameworks. Ethical concerns surrounding bias, transparency, and privacy are underexplored, and few studies provide robust models for collaborative work between humans and AI. This review identifies the need for longitudinal research, structured participatory frameworks, and ethically grounded design methodologies. The paper contributes a comprehensive synthesis of current knowledge and outlines directions for future inquiry at the intersection of generative AI and human-computer interaction.

Tarika Kumar, Xinyi Tu, Matteo Zallio

Conference Proceedings