AHFE Open AccessHuman Interaction and Emerging Technologies (IHIET 2026)
Editors: Tareq Z. Ahram, Luca Casarotto, Pietro Costa
Topics: Artificial Intelligence & Computing, Human Systems Interaction
ISBN: 979-8-950676-12-3
DOI: 10.54941/ahfe1008075
Table of Contents
Explainability in Automated Driving: From Spatial Attention to Human-Centred Reasoning
Automated Vehicles (AVs) are developing rapidly and promise to improve road safety. AV systems are equipped with advanced AI techniques to perceive, learn, decide, and act, yet the decision-making process underpinned by complex AI constitutes a black box for human users. While human understanding of AI-based decision-making is critical to building trust, acceptance, and efficient human-machine cooperation, existing algorithmic approaches provide cause-effect relations in decisions but fail to account for the psychosocial and cognitive dynamic nature of supervising an AV. There is a lack of comprehensive framework addressing this algorithmic transparency with the cognitive and affective dynamics of the human recipient, leaving the human side of the explanation transaction theoretically underdeveloped. We propose a human-centred explainability framework that repositions AV explanation as a cognitive human-machine interaction problem. The framework is operating across spatial, semantic, and cognitive registers. It articulates where a system attends, what it recognises, and why that recognition contributes to better explanation. These levels integrate semantic awareness to enrich causal reasoning, while spatial information contextualise explanations. The preliminary framework is grounded in naturalistic field observations in instrumented vehicles, collecting video data and field notes complemented by post-drive semi-structured interviews. Thematic analysis yielded a driving context-specific explanatory vocabulary, formalising conditions under which users seek, process, and integrate AV explanations. Drawing on cognitive science and human factors theory, we derive design principles for adaptive explanation delivery, leveraging multimodal large language models as the generative engine for contextualised natural language explanations responsive to user expertise and situational urgency. This work demonstrates that the next frontier in AV explainability should be more aligned with human cognition.
Andry Rakotonirainy, Ashkan Y Zadeh, Zishuo Zhu, Djamel Benrachou, Mohammed Elhenawy, Sebastien Glaser, Xiaomeng Li, Ronald Schroeter, Melaine Gouillou, Patricia Delhomme
Open Access
Article
Conference Proceedings
Design and Evaluation of an AI Academic Advisor: Insights from Student Interactions
Artificial intelligence (AI) is increasingly being explored to improve the efficiency and accessibility of academic advising in higher education. Traditional advising systems often face challenges, including limited advisor availability and increasing administrative workload, which can delay student support. This study presents and evaluates a conversational AI academic advisor built using a Retrieval-Augmented Generation (RAG) architecture that combines a large language model (LLM) with a curated knowledge base. The system allows students to ask advising-related questions and receive responses grounded in official documents. A pilot study with 20 university students was conducted in which participants interacted with the system to explore tasks for advising tasks before completing a usability survey. Results indicate generally positive perceptions of the system’s convenience and accessibility, with some participants reporting that the system was easy to use and helpful for obtaining information, and some indicating that responses were clear and understandable. Students highlighted the speed and immediacy of responses as key advantages. However, some participants reported response delays and limitations in handling complex questions. Overall, the findings suggest that AI advisors can effectively complement traditional advising services for routine inquiries.
Aysha Almazrouei, Afra Almazrouei, Haseena Alkatheeri, Mohammad Amin Kuhail
Open Access
Article
Conference Proceedings
To boldly go where AI must not go alone: Designing for non-delegable human authority in AI-assisted expert work
AI-assisted expert work raises a recurring question for human-machine systems design: where must human authority remain structurally located, rather than delegated to behavioural monitoring of the automated system? Conventional oversight is operationalised as post-hoc error detection, which the human factors literature shows fails under complacency and vigilance decrement at high reliability. We propose a design pattern that re-frames oversight as a structural property of the design process rather than a behavioural demand on the operator. We define Non-Delegable Points (NDPs): procedural stages whose constitutive quality criteria are human-authored and which resist delegation regardless of agent capability. For qualitative content analysis, we identify three NDPs – domain-knowledge curation, iterative codebook refinement, and communicative validation – and present an architecture enforcing them through codified process steps, immutable knowledge layers, and strict separation of a development agent from an application agent. The architecture suggests reproducible per-step audit artefacts without depending on reasoning-chain faithfulness. We anchor the pattern in the automation-monitoring tradition and link it to the EU AI Act's requirement for effective human oversight (Article 14). The contribution offers a transferable model for AI-assisted expert work in which human authority is enforced by design.
Raimund Lehle, Julia Kaesmayr
Open Access
Article
Conference Proceedings
Integrating Three Modalities into One Experience: A Case Study of 2024 DigiWave—DdDd
Exhibition design today is moving towards multimodal experience and towards deepening the visitor's participation. Visitors meet physical interfaces in the built space, mobile interfaces on their own devices, and embodied experiences that come from how the body moves, attends and acts. Whether an exhibition succeeds depends less on any single modality than on how well the three are joined, so that the visitor follows one coherent journey instead of three parallel ones. This paper analyses 2024 DigiWave—DdDd, an exhibition on the cultural evolution of sound and digital media presented under Taiwan’s Ministry of Digital Affairs and recognised in Shopping Design’s Taiwan Design Best 100 awards, as a case in which three modalities were integrated by design. It examines how perforated melamine wall structures (physical interface), a mobile puzzle that ends in a personal digital instrument (mobile interface), and a final interactive sound installation in which visitors collectively shape music and projection (embodied experience) were designed to act as one experiential arc. From this case the paper derives four design principles: each modality should express the exhibition’s core metaphor in its own register; transitions between modalities should be staged as discoveries rather than procedural handoffs; the mobile interface should add agency rather than draw attention away from the physical space; and the closing experience should set the device aside and return the body to the centre. The paper closes with implications for designers working across these modalities in cultural and brand exhibitions.
Liting Huang
Open Access
Article
Conference Proceedings
Improving Usability in a Smart Building Ecosystem through Heuristic Evaluation and Usability Testing
The design of smart-building digital ecosystems presents challenges when multiple interfaces must serve users with distinct roles, expertise levels, and operational contexts. Rather than focusing solely on the identification of performance deficiencies, this study adopts an interpretive approach aimed at generating real-use-based insights for the design of multi-interface systems. Three interfaces of a smart-building software ecosystem were evaluated: the End-User App, the Management Software, and the Worker App. A heuristic evaluation based on Nielsen’s ten usability heuristics and ISO 9241-110 principles was combined with a usability study involving eight participants who performed representative tasks under a think-aloud protocol. Sessions were recorded and analyzed through thematic analysis. The integration of these two methods reveals a coherent and mutually reinforcing picture: heuristic violations identified prior to testing, including insufficient system feedback, inconsistent iconography, and poor information architecture, were consistently confirmed by user behavior and verbalizations. The Worker App demonstrated strong task-oriented usability, while the End-User App was generally accessible but exhibited specific interaction gaps. The Management Software presented the steepest learning curve among the three interfaces. Cross-cutting themes point to ecosystem-level design opportunities. The study contributes evidence-based recommendations framed as a practical resource for iterative interface improvement, and illustrates how combined heuristic and usability evaluation constitutes a methodologically robust approach to the human-centered assessment of complex multi-interface systems.
Rosana Alexandre, Filipe Moreira, Andre Cardoso, Manuel Alves, Ricardo Rodrigues, Ana Colim
Open Access
Article
Conference Proceedings
Anchored in the Learner: A Critical Review of AI Discourse in Design Education
Generative AI is moving quickly into design education, universities and design schools have already issued guidelines, frameworks, and curricular advice. Across this work, three concerns stand out. The first is building students' AI literacy, the second is the effect of AI on the design profession, the third is redesigning teaching for the new tools. What receives far less attention are the student’s psychological needs, such as the need for autonomy, relatedness, competence when using AI for a design task. We argue that good educational frameworks should be anchored in the learner, not driven by technology. Following a scoping review approach, we read the recent design-education discourse on generative AI from 2022 to 2026 together with the established work on learner experience, psychological needs, and wellbeing in HCI and AI. Together they show a clear pattern. Related works are diverse but pay little attention to what students actually need. When needs do appear, they are used mainly to explain outcomes such as creativity or critical thinking. We argue that students' needs matter on their own, the field should start from what students need when deciding how to use AI in design education. Our contribution is not a new framework but a change of stance that puts the learner's needs first.
Antong Zhang
Open Access
Article
Conference Proceedings
Narrative as a Cognitive Scaffold for Human-Centered Design Education:A Case Study of Schema Change in Interaction Design Students
Human-centered design (HCD) education has long faced a gap: while students can readily produce ideas, they struggle to integrate scattered concepts into coherent interaction design works. This study examines whether narrative pedagogy can help interaction design students develop such integrative cognitive capacity, with two aims: (1) to analyze how students' creativity schemas across the four ION dimensions change under narrative pedagogical intervention, and (2) to examine the role of narrative pedagogy as a cognitive scaffold in shaping students' design thinking. The study adopts Kim's CATs model as its primary framework, using ION thinking combined with Creative Attitudes, and draws on Meisiek et al.'s schema-change modes for describing cognitive change. Situated in an eight-week Story Creativity and Narrative Design course at National Yunlin University of Science and Technology, Taiwan, this qualitative multiple-case study tracked three interaction design students through pre-, mid-, and post-course semi-structured interviews, with intercoder reliability checked across three independent coders. Findings reveal changes across all four dimensions: the basis for judging work shifted from personal feeling to design purpose; the starting point for ideation shifted from direct personal association to first defining the audience and then constructing content; the way of organizing design elements shifted from juxtaposition to causal chaining; the use of emotion and playfulness shifted from passive reception to active deployment for conveying messages. Among these, metaphorical thinking was the only capability emerging during the course, while others were refined mid-course and internalized post-course, persisting after the instructor's scaffolding was withdrawn — this temporal pattern provides concrete empirical support for the claim of narrative as scaffold, and this structure aligns with the integrative capacity required by interaction design practice. The study therefore argues that narrative in HCD education should be reframed from a communication skill to a scaffold for design cognition; for design educators, this means narrative practice can serve as a practical pedagogical intervention for cultivating integrative design capacity.
Shih-Ping Chiu, Wen-Huei Chou
Open Access
Article
Conference Proceedings
Assessment-Before-Intervention: A WHO iSupport-Grounded Conversational AI System for Dementia Family Caregiver Support
Family caregivers of people with dementia face daily behavioral challenges—aggression, wandering, agitation—that require timely, contextual guidance. Existing chatbot systems typically respond with direct advice, bypassing assessment of behavioral context and caregiver emotional state. This paper presents a LINE-deployed conversational AI system grounded in the WHO iSupport for Dementia framework, built around an Assessment-Before-Intervention dialogue mechanism. The system applies a four-step reasoning process derived from the iSupport ABC behavioral cycle, governed by five safety principles, to determine when to clarify before advising. A dual-layer response model ensures emotional acknowledgment is never omitted; a hybrid keyword-semantic Retrieval-Augmented Generation (RAG) architecture bridges the lay-to-clinical vocabulary gap. We evaluated the system through a formative review with four domain reviewers in dementia care, covering 13 BPSD scenarios (52 evaluations across five quality dimensions). Mean scores (4.78–4.81 on a 5-point Likert scale) are interpreted as preliminary perceived-appropriateness data rather than clinical effectiveness evidence. The principal contribution lies in the qualitative findings: five recurrent failure modes and a structural pattern of cultural misalignment between the international iSupport framework and Taiwanese caregiving realities. Findings offer practical implications for the design of AI-assisted care systems in dementia and other emotionally sensitive healthcare contexts.
Chor-Kheng Lim
Open Access
Article
Conference Proceedings
Analyzing Stress and Perceived Safety in Human-Cobot Collaboration: The Impact of Task Proximity, Interface Cues, and System Errors
Collaborative robots (cobots) are increasingly deployed in shared workspaces, where effective interaction depends not only on physical safety but also on operator trust, transparency, and comfort. This study investigates how collaboration structure, feedback modality, and system reliability affect task performance, user experience, trust, perceived safety, and physiological responses during human–cobot interaction.Eighty participants performed collaborative assembly tasks under two scenarios: a low-collaboration turn-taking task and a high-collaboration synchronous task. Feedback conditions included visual, auditory, multimodal auditory–visual feedback, and a Projected Assistive Interface (PAI) in the high-collaboration scenario. In addition, controlled anomalies, including robot failures, communication errors, and motion errors, were introduced to evaluate system resilience.Subjective evaluations, task completion times, and heart-rate variability (HRV) measures were collected. Results show that feedback effectiveness depends strongly on both collaboration structure and system state. Multimodal feedback and PAI improved task understanding, comfort, and trust, particularly during high-collaboration tasks. Preliminary results indicate that system errors reduced trust and perceived safety while eliciting measurable physiological responses. Motion errors produced stronger stress reactions, whereas robot failures primarily reduced perceived reliability. Transparent multimodal feedback also facilitated trust recovery following error events.These findings suggest that feedback mechanisms should be designed not only to support task execution but also to maintain trust and resilience under imperfect operating conditions. The study provides practical design guidelines for human-centered collaborative robotic systems operating in both normal and failure conditions.
Shraga Shoval, Amir Biton, Yuval Cohen
Open Access
Article
Conference Proceedings
Metascience of content-based cognitive ergonomics
In the ongoing technological revolution from 4.0 to 5.0 life, a critical issue is cognitive ergonomics for intelligent information processing. Joint Cognitive Systems (JCS) is a helpful concept to understand the strengths and limitations of human and machine information processing and reasoning. To operate in the real world, the JCS must represent the relevant actions, agents, goals, and context. However, machine representations such as Turing machines or formal automata represent the world in terms of meaningless symbols. Such formal machines must be interpreted, i.e., their representational elements and operations that generatively manipulate representations must be given meanings, which is often called symbol grounding or interpretation. The representational elements must be given meanings, otherwise they cannot function in a meaningful way in the real world. It seems that information content and its relevance are a consequence of human information processing and thinking. An interpreted Turing machine has a set of domain-specific rules or operations that manipulate symbolic states from one to another. Information content makes it possible to ask and answer new kinds of questions, for example about truth and validity. Information content and its relevance thus form the human part of intelligent JCS. The problem of information content, its relevance and truth form the standpoint of a new kind of ergonomic thinking. It focuses on the content of relevant information. This field of ergonomics can be called content-based, because the topic of its analysis as well as its arguments are based on the content of information processes in human-machine interaction.
Pertti Saariluoma, Mari Myllylä, Jose Canas
Open Access
Article
Conference Proceedings
Developing an Assistive Mobile Application for Elderly Nepali Migrants in the UK
Over 77,000 Nepal-born residents live in England, many of whom are elderly Gurkha veterans who arrived following the 2009 Gurkha Justice Campaign. Despite over 200 years of military service to the British Crown, this population continues to face significant challenges adapting to daily life due to limited English proficiency, low digital literacy, and a lack of technology designed with their needs in mind. This study adopted a Human-Centred Design (HCD) methodology to design, implement, and evaluate Jana, a mobile application tailored to support older Nepali migrants in the UK. A survey with 68 participants identified complex navigation and literacy difficulty as the two primary barriers to mobile technology use, informing iterative low and high-fidelity prototyping evaluated by seven target demographic participants using the think-aloud protocol. Jana combined Nepali-English translation via camera and voice input, one-touch emergency service access, a community feed, and a Bikram Sambat calendar, built using React Native. A heuristic audit and summative task-based evaluation identified three recurring themes: translation, linear navigation, and icon comprehension, with measurable usability improvements across iterations. Six of seven participants confirmed they would use the application daily. The study produced six transferable design principles offering practical guidance for developers creating digital tools for migrant communities facing overlapping cognitive, literacy, and cultural barriers to digital inclusion.
Prakriti Rai, Gail Hopkins
Open Access
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Conference Proceedings
Integrated Home Service Robots for Ageing in Place: A Multi-Stakeholder Perspective on Acceptance and Care Needs in Taiwan
As population ageing and ageing in place become increasingly important, home service robots (HSRs) have been discussed as potential support for domestic care. However, HSR acceptance in home settings involves not only older adults, but also family members and care workers. This study explores multi-stakeholder needs and concerns regarding HSRs in Taiwan through semi-structured interviews with six information-rich participants: two older adults, two family members, and two care workers. Findings show that stakeholders valued practical functions such as medication reminders, health monitoring, safety alerts, and support for daily routines. However, acceptance differed by role. Care workers emphasized workflow fit and early risk detection, family members valued reassurance and remote support, while older adults stressed autonomy, control, privacy, and low operational burden. The study suggests that HSRs should be designed as assistive tools that fit everyday care practices, domestic space, and existing family relationships rather than as replacements for human care.
Yuhsin Chen, Wenhue Chou
Open Access
Article
Conference Proceedings
From Retrieval to Verification: An Agentic Framework for Rule-Aware Engineering Document Compliance
Large-scale engineering projects generate continuous streams of compliance-critical documents, including material submissions, method statements, inspection and test plans, safety data sheets, and contractor certificates. Each must be verified against project specifications, regulatory codes, and contractual requirements. Current manual expert review is slow, inconsistent, and provides limited audit depth. Existing AI approaches typically use Retrieval-Augmented Generation to retrieve relevant clauses but do not conduct structured, rule-bound verification. This paper presents an agentic document verification framework that moves beyond passive retrieval to active, rule-aware compliance checking. The system uses a semantically indexed knowledge base built from project specifications, regulatory standards, and historical approval records. A dedicated Verification Agent decomposes documents into structured claim units, including numerical parameters, referenced standards, tabular test results, and graphical certificates. These are evaluated against dynamically constructed project rule sets using a Chain-of-Thought inference pattern. The framework generates compliance reports with pass, fail, or query verdicts, confidence scores, and traceable evidence bindings for each decision point. Material submissions are the primary validation domain because their dense technical content, cross-referenced tables, graphs, and third-party certificates rigorously test multi-modal parsing and verification capabilities. Validation used an industrial pilot across 10 live projects in the Electrical and Mechanical engineering sector. Results from 63 processed submissions show a 70.9% reduction in average review time, from 52.5 minutes under the existing digital workflow to 15.3 minutes with the AI-assisted system. Results also show an 88% system agreement rate, with human overwrites required in only 12% of verdicts. By incorporating a Propose-Decide-Evidence governance model, the system retains the human engineer as final decision-maker while establishing an efficient, auditable, continuously improving compliance workflow.
Ka Tai Lau, Man Chit, Jovian CHEUNG, Lok Him TSE, Pok Man SO, Yabing HOU
Open Access
Article
Conference Proceedings
Architecting Digital Educational Escape Rooms: A Framework for Instructional Orchestration, Role-Based Collaboration, and AI-Supported Learning
Digital Educational Escape Rooms (DEERs) have increasingly emerged as innovative technology-enhanced learning environments capable of promoting learner engagement, motivation, and collaborative participation. Nevertheless, their instructional effectiveness may remain limited when collaboration, progression, and learner support are not systematically orchestrated within the architecture of the environment itself. Existing implementations often emphasize gamification elements without embedding pedagogically aligned mechanisms that regulate interaction flow, interdependence, and reflective learning processes.Objective:To design, develop, and examine a system-oriented Digital Educational Escape Room environment that integrates conditional progression, role-based collaboration, and AI-supported scaffolding through an instructional orchestration framework grounded in the MCIEC model.Methodology: The study adopted a design-based and system-oriented approach focusing on the architectural and technical implementation of the Find Mr. X Digital Educational Escape Room. The environment was developed on the Wix platform using Velo Dev Mode and implemented into an event-driven learning system through code-based interaction management. The instructional design was structured according to the five phases of the MCIEC model (Motivation, Context, Interactivity, Evaluation, and Connectivity). Learner progression was regulated through hidden navigation, conditional redirection, event triggers, and validation mechanisms ensuring task-dependent advancement. Furthermore, the Jigsaw collaborative strategy was operationalized through differentiated expert pathways and role-based access structures, while Artificial Intelligence tools were embedded as optional scaffolding resources supporting reflection, verification, and collaborative inquiry.Results: Findings from a pilot implementation involving 30 pre service an in service teachers demonstrate the operational reliability and pedagogical feasibility of embedding instructional orchestration directly into the system architecture, resulting in a 100% completion rate with no navigation errors. Participants positively evaluated the system’s contribution to mathematical understanding and learning (M = 4.30/5.00), while the embedded AI-supported scaffolding layers were similarly perceived as effective support mechanisms (M = 4.13/5.00). Conditional progression mechanisms successfully reduced unstructured trial-and-error behaviors by enforcing meaningful sequential interactions, while the role-based Jigsaw structure promoted positive interdependence and collaborative knowledge synthesis. Finally, the study contributes a transferable design framework linking instructional affordances with platform-level implementation mechanisms, including hidden navigation, visibility constraints, event-driven interactions, and collaborative orchestration patterns.
Vasiliki Konstantopoulou, Foteini Paraskeva
Open Access
Article
Conference Proceedings
Affordance Detection in Atypical Architectural Spaces Using Behavior Pattern Recognition
Atypical architectural spaces present significant challenges for predicting and evaluating human behaviors during the architectural design process. Conventional human behavior simulation methods often rely on predefined rules or agent-based pathfinding approaches, which have limitations in representing the complex relationships between spatial form and potential human actions in highly irregular environments. To address this issue, this study proposes an affordance detection approach for atypical architectural spaces using behavior pattern recognition. The proposed method focuses on extracting geometric patterns associated with human behaviors and utilizing them to identify spatial affordances in newly designed spaces. First, spatial locations where human behaviors are successfully placed in atypical environments are analyzed to extract surrounding geometric configurations. These configurations are formalized as behavior-inducing geometry patterns and organized into a structured behavior pattern database. Each geometry pattern is linked to a corresponding human behavior model, establishing a relationship between spatial form and potential human actions. Based on this database, behavior pattern recognition is applied to analyze newly designed atypical architectural spaces by searching for geometric configurations similar to the stored patterns. When a matching pattern is detected, the associated human behavior model is automatically assigned to the corresponding spatial location. Through this process, the system identifies spatial regions where specific human behaviors are likely to occur, effectively detecting spatial affordances in atypical architectural environments. Implemented within the Rhino and Grasshopper computational design environment, the proposed approach supports behavior-informed architectural design by enabling designers to evaluate potential human behaviors during the design process.
Yun Gil Lee
Open Access
Article
Conference Proceedings
Toward Reinforcement Learning for Selection and Parameterization of Appropriate Prevention Measures for Collaborative Robotic Applications
The deployment of collaborative applications has emerged as a key trend in the manufacturing industry, driven by the synergy of human intelligence and adaptability with robots' endurance, strength, and precision. However, this closer integration of humans with robots heightens the risk of injuries. To prevent these risks, the risk reduction process is performed during design phase of collaborative workstations, ensuring that safety criteria required by current standards are met. In this work, we formulate the risk reduction process as a sequential decision-making problem and propose a Reinforcement Learning-based approach. In a simulation environment, the RL agent learns to select and parameterize prevention measures to reduce collision and crushing risks of collaborative applications. The considered prevention measures include physical barriers, laser safety barriers, and Tool Center Point (TCP) speed monitoring zones parameterized by their geometric and temporal activation interval parameters. The RL agent is trained through trial-and-error interactions within the simulated environment, aiming to maximize a reward function that prioritizes risk reduction. Secondary objectives include minimizing cycle time and reducing workstation footprint, allowing the system to derive optimized solutions that satisfy safety while balancing cost and productivity constraints. A feasibility demonstrator is presented through a use case in which the RL agent proposes prevention measures to reduce identified hazards for a single collaborative application, enabling faster human-centered design of safe collaborative workstations.
Gustavo Afonso Novak, Vincent Weistroffer, Jonathan Savin, Richard Bearee
Open Access
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Conference Proceedings
Could AI-Chatbot help with prevention of burnout syndrome? Pilot study in two Czech manufacturing companies.
Burnout syndrome develops as a result of prolonged overload combined with insufficient recovery. It manifests as emotional and physical exhaustion as well as cognitive fatigue. In the modern era, numerous digital approaches are available for the prevention and promotion of health, including mental health. A pilot project aimed at verifying the feasibility of burnout prevention using a professionally supervised chatbot was conducted from January to July 2025. Collaboration was established with two manufacturing companies, and based on baseline questionnaires—particularly the SMBM (Shirom–Melamed Burnout Measure)—30 respondents were selected. Out of 71 respondents, 63 SMBM questionnaires were analyzed, yielding a mean score of 51 points, indicative of mild to moderate burnout. Over an 8-week period, chatbot communication was enabled for the 30 selected participants. A total of 22 respondents engaged with the chatbot. The follow-up SMBM questionnaire was completed by 10 respondents (response rate 30%). Due to the low number of responses, group-level evaluation was not feasible; however, improvement was observed in 6 individual cases. A professionally designed and supervised digital tool—a chatbot utilizing artificial intelligence—appears to be a highly promising instrument for large-scale prevention of work-related burnout. Further validation in practice is required.
Vladimira Lipsova, Karolina Mrazova, Kateřina Bátrlová, Martina Sebalo Vnukova, Zdenek Musil, Vladimir Musil, Nina E. Carroll
Open Access
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Lamp Designs and Attention Fields: Ambient Computing for Agentic AI
As artificial intelligence becomes integrated into an increasing number of everyday objects, our relationships with these objects will inevitably evolve. The built environment—including architecture, furniture, and even the lighting that shapes much of our lived experience—offers new opportunities to reconsider how we interact with everyday objects. As with other emerging AI-enabled technologies, these objects may shape our attention in fundamental ways. This design research project asks: How might objects that hide in plain sight initiate meaningful interactions with users and appropriately calibrate users’ attention while still fulfilling their conventional role as furniture? To explore this question, we developed a series of lamp prototypes that use light and movement to initiate attention within a given space. We speculate about the development and proliferation of “Attention Fields,” in which objects situate themselves as constellations of AI-embodied artifacts that respond to the presence of users within a space.
Ian Gonsher, Jialong Lai, Runan Wang, Ruijia Diao, Shaivi Tomar
Open Access
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Conference Proceedings
Augmented Intelligence and Creative Dialectics: Developing Tools for Enhanced Creativity
The creative process can be characterized as a creative dialectic. Creative dialectics can be understood in terms of the development of novel and useful products that emerge through a process of contradiction and synthesis. In this paper, we present and evaluate a pilot prototype for a tool that allows users to navigate this dialectical process in order to enhance their creative process. The development of such a tool also offers a model for AI alignment, eschewing Agentic AI in favor of augmented intelligence; a paradigm that emphasizes collaboration between machine and human in order to generate new ideas and implement them in ways that are relevant to the appropriate context. The prototype is implemented as a node-based ideation system that helps designers to generate, connect, and refine ideas through modular interactions. Key features include a critique mode that embeds systematic frictions into the creative process, a synthesis engine that supports the combination and development of ideas, and a design log that saves and keeps track of users’ reflections and iterative thinking. To evaluate the system, we conducted a workshop-based user study (n=12) in which participants completed an open-ended design task and were evaluated based on their creative process and design outcomes using the system. The findings suggest that the system primarily supports creativity through the development of and reflection on ideas, rather than directly generating novel outputs.
Ian Gonsher, Changfeng Wang, Bozhou Pang, Liu Hu, Shuyi Mao
Open Access
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Conference Proceedings
A Modular Edge-to-Cloud Architecture for Remote Monitoring and Condition-Based Maintenance in Scalp Cooling Cyber-Physical Systems
Scalp cooling (SC) systems operate as refrigeration-based therapeutic cyber-physical systems in which deterministic embedded control, closed-loop thermal regulation, and multi-sensor acquisition must satisfy strict real-time performance, reliability, and medical software compliance requirements. Existing embedded architectures for such systems primarily function as isolated controllers that provide local temperature regulation but lack system-level telemetry, condition-based health monitoring, and traceable compliance verification across treatment cycles. This work introduces a modular edge-to-cloud architecture for connected medical refrigeration cyber-physical systems that preserve deterministic embedded control while enabling device observability and fleet-level analytics. The proposed framework integrates heterogeneous sensor acquisition with differentiated sampling cadences, including 3-second synchronous monitoring of coolant tank temperature and flow rate, event-driven treatment stage capture, and low-frequency coolant pH monitoring. Fault-tolerant local persistence is achieved through an embedded SQLite datastore with periodic batch synchronization to AWS cloud infrastructure. In addition, the system enables automated compliance verification against Instructions for Use (IFU) treatment-stage constraints and supports condition-based maintenance analytics derived from thermal performance, coolant quality, and device utilization metrics. The architecture was evaluated across fifteen controlled deployments at two sites under ambient conditions ranging from 14.7-26.0°C. All deployments successfully achieved the operational temperature of −4°C, demonstrating reliable and deterministic thermal control under heterogeneous environmental conditions. The proposed framework provides a scalable retrofit pathway that enables legacy refrigeration-based medical devices to transition into connected, observable, and compliance-verifiable cyber-physical system fleets suitable for large-scale clinical deployment.
Tauqeer Ali Khan, Omar Huerta, Dipo Olaosun, Jonathan Biner, Ertu Unver, Peter Culmer
Open Access
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Conference Proceedings
AI Collaborative and Active Learning for GIS Course with School Bus Routing Project
The rapid proliferation of generative artificial intelligence (AI) has necessitated a paradigm shift in higher education, particularly within Geographic Information Systems (GIS) pedagogy. Traditional models often fail to bridge the gap between abstract spatial theory and the technical frictions of real-world implementation. This study evaluates a Project-Based Learning (PBL) framework augmented by AI—acting as a "co-pilot"—to enhance student engagement and technical mastery. Centered on the Xinxing High School School Bus Routing Problem (SBRP), the project involved the high-precision scheduling of 1,124 students across 124 distinct demand nodes. By leveraging AI-augmented optimization, the research demonstrates significant operational dividends: fleet consolidation from 41 to 32 vehicles, a 19.3% reduction in carbon emissions, and the achievement of 100% punctuality. Furthermore, this collaborative approach addresses critical academic integrity concerns by transitioning from generic assessments to "Authentic Assessment," requiring localized data-driven reasoning and human-centric trade-offs.
Ming-der May
Open Access
Article
Conference Proceedings
A Multimodal, Uncertainty-Aware, and Transparent AI Grading Tool for Scalable Automated Grading in AI and Data Science Higher Education
Multimodal assignments- programming, free response, and oral - are difficult for humans to grade consistently at scale. Artificial Intelligence (AI) assisted Automated Grading Tools (AGTs) offer a path forward, but existing systems are evaluated within a single modality and lack a framework for routing assignments to human review based on AI uncertainty and modality. This paper evaluates a multimodal AGT on 95 assignments: Programming (n=35), free response (n=30), and oral (n=30), each scored by two human graders and the AI. This paper presents three novel contributions to the field of AI assisted AGT. First, it contributes a per-modality validity characterization including the first directionally-resolved test on mock-interview grading. Second, it presents the exemplar-anchored harshness hypothesis as a falsifiable mechanism for AI severity on subjective modalities. Finally, it provides an empirical test of semantic-entropy escalation finding no usable routing signal, based on the sample size and experiment settings.
Olivia Dias, Cynthia Breazeal, Kantwon Rogers
Open Access
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Conference Proceedings
Virtuality and Reality in Designing Future Services that Utilize Past Episodes
The success of AI services is predicated on the availability of relevant data and their acceptance by users. However, AI service design often faces a dual challenge: the opacity of the past, where critical historical data remains inaccessible, and the opacity of the future, where the value and risks of new technologies are difficult to envision before adoption. These challenges are particularly evident in services that aim to utilize personal histories and life experiences that have not been digitized, as well as in emerging AI applications whose practical benefits and limitations are not yet fully understood by prospective users. To address these issues, we propose an analytical framework derived from Virtual Reality (VR) design—spatiality, interactivity, and self-projection—and apply it to the design and evaluation of a narrative-based care ecosystem.We exemplify this approach through two grounded case studies within the care sector. First, we conducted a narrative collection experiment involving an elderly individual with dementia. The participant walked through familiar locations while engaging in conversations with a care worker. The results demonstrated that aligning environmental contexts with empathetic interaction can elicit latent personal histories that are otherwise difficult to access. Familiar surroundings acted as memory triggers, while confirmation-based communication and positive feedback encouraged spontaneous narrative disclosure. Analysis using the proposed framework revealed that effective narrative collection depends on the construction of a cognitive space where past and present coexist, supportive interaction that connects fragmented memories, and opportunities for individuals to project their authentic selves through storytelling. These findings suggest practical approaches for overcoming the opacity of the past and improving the collectability of personal data required for AI services.Second, we evaluated a Proof of Concept (PoC) for an AI-based care advice tool with experienced care staff. The tool referenced narrative profiles of care recipients and generated individualized communication advice. While participants appreciated the tool’s ability to organize information, provide objective perspectives, and support personalized care, the evaluation also highlighted a critical dissonance between designer-focused technical accuracy and the operational reality of frontline care. Staff emphasized concerns related to mobility, workflow integration, timing of interaction, privacy, emotional labor, and the need for multiple alternative suggestions rather than a single optimal answer. Analysis through the VR framework revealed differences between how designers and practitioners envisioned the service environment, expected interactions, and the role of AI in care decision-making.These cases provide universal insights applicable to broader AI service design. To overcome the opacity of the past, designers must create interfaces that ensure psychological safety, establish trust regarding data use, and employ contextual cues that facilitate spontaneous data disclosure. To mitigate the opacity of the future, AI should be positioned not as a replacement for human expertise but as a co-creation partner that offers objective insights while preserving the user’s decision-making sovereignty. Furthermore, successful AI adoption requires attention to physical work environments, temporal continuity, and the contextual realities of everyday practice. By shifting from feature-centric design to an approach that respects the continuity of user context, personal narratives, and professional expertise, designers can successfully address both forms of opacity and foster sustainable AI integration across diverse industries.
Masayuki Ihara, Hiroko Tokunaga, Tomomi Nakashima, Hiroki Goto, Yuuki Umezaki, Yoko Egawa, Shinya Hisano, Takashi Minato, Yutaka Nakamura, Masashige Motoe, Shinpei Saruwatari
Open Access
Article
Conference Proceedings
Meta Ray Ban Smart Glasses - Watching the Watchers.
Smart glasses are emerging as consumer grade wearable technologies that integrate cameras, sensors, connectivity, and, in some cases, augmented reality displays into everyday eyewear. While these devices offer hands free, context aware interaction and potential benefits for users, they also raise concerns related to surveillance, privacy, and social acceptability. This study examines citizens’ attitudes toward artificial intelligence (AI) and smart glasses, with particular emphasis on perceptions of surveillance and intentions to adopt protective or privacy enhancing technologies. Drawing on two nationally representative Norwegian surveys conducted in 2020 with 1290 respondents and 2026 with 1086 respondents, the study analyses changes in general attitudes toward AI following the widespread adoption of tools such as ChatGPT, as well as public agreement with six AI related concerns identified by experts in the Pew Research Center’s AI100 project. In addition, the study investigates predictors of citizens’ intentions to use technologies that alert them when smart glasses are recording in their vicinity. The findings show that general attitudes toward AI have become more positive over time, especially among younger age groups. Nevertheless, a majority of respondents have concerns regarding the societal implications of AI, including threats to human agency, corporate data abuse, misinformation, and loss of control. Using binary logistic regression, the analysis identifies concerns about corporate data abuse and fake news and propaganda as significant predictors of intentions to use warning technologies, alongside demographic variables, overall attitudes toward AI, and individual characteristics associated with early technology adoption. Overall, the results highlight a persistent tension between growing acceptance of AI enabled technologies and sustained concerns about surveillance and privacy.
Ingvar Tjostheim
Open Access
Article
Conference Proceedings
EnviPeace – A MR-based Learning System for Environmental Peacebuilding
Climate change, pollution, and environmental degradation have significant effects on human well-being and ecosystems, resulting in extreme weather events and loss of biodiversity and livelihoods. Climate change poses a threat to peace and stability in states and societies. The majority of the countries most affected by climate change also experience armed conflicts, which increase vulnerabilities that can be further exacerbated by resource scarcity and environmental degradation. “Environmental peacebuilding” (EP), is an approach that aims at creating sustainable peace by integrating climate and environmental considerations into conflict prevention, management and resolution. This requires cooperation among various actors, such as governments, NGOs, international organizations, and local communities. Civilian and military experts working in crisis areas must acquire necessary knowledge and skills to incorporate these factors into their work. However, it is difficult to create realistic training situations since environmental and climate factors and their effects on crisis areas cannot be visualized in real-life simulation exercises. The integration of mixed reality (MR) technologies into the training process enables more realistic and immersive simulations of the effects of climate change, scarcity of resources, and environmental degradation. This helps the trainees to better understand the perspectives of the communities affected as well as the operational tasks and challenges faced by peacebuilding personnel deployed in contexts affected by climate change and conflict. EnviPeace provides an MR-based learning environment that enables teaching advanced peacebuilding and conflict transformation skills. It is designed for large outdoor areas, where virtual elements such as floods, droughts, local inhabitants, or refugee camps are overlaid onto the real world through MR glasses. Combining live actors with virtual characters creates rich, adaptive simulations. This paper describes the current state of the EnviPeace system, the results of the requirements analysis and the first results of the scenario tests.
Elisabeth Broneder, Simone Formica, Christoph Weiß, Jaison Puthenkalam, Sebastian Egger-Lampl, Markus Karlseder, Astrid Holzinger, Monika Psenner, Juliana Krohn
Open Access
Article
Conference Proceedings
Human-AI Role Allocation in Design Thinking: Contributions to Initiation, Elaboration, and Decision-Making
Small and medium-sized enterprises (SMEs) operate under persistent resource constraints and face growing pressure to innovate in an increasingly dynamic and digitalised market environment. In this context, generative artificial intelligence (AI) is emerging as a valuable tool for knowledge-intensive and creative work. However, its value does not arise from technological availability alone, but from how it is embedded in organisational processes, team interactions, and the allocation of roles between human actors and AI. This paper examines the role of ChatGPT as an AI assistant in team-based Design Thinking, with particular attention to how human–AI role allocation can be integrated into SME innovation processes.Drawing on concepts of hybrid intelligence and human-centred innovation, the central research interest lies in understanding how roles can be allocated between humans and AI across different stages of the innovation process without replacing human judgement, contextual understanding, and responsibility for final decisions.An exploratory experiment was conducted with 32 working professionals collaborating with ChatGPT in pairs across four Design Thinking phases (Brown, 2008): Empathise and Define, Ideate and Decide, Prototype, and Test. Within a 60-minute session, participants developed an innovation concept for a fictitious Vienna-based company offering a hydroponic planting system for indoor and balcony use. The setup compared two interaction sequences: in Team A, an AI–Human–AI pattern in which the AI initiated and supported preselection; in Team B, a Human–AI–Human pattern in which humans framed the task and retained final decision-making authority.Methodologically, the study combines standardised questionnaires (NASA-RTLX, SUS), open-ended responses, and documented chat transcripts; the present paper reports descriptive findings only. By focusing on human–AI role allocation within teams rather than on individual AI use, the paper contributes to current discussions on how SMEs can integrate generative AI into innovation processes in a purposeful, human-centred and methodologically reflective way.
Isabel Rodenas, Patrick Rupprecht
Open Access
Article
Conference Proceedings
AI-Supported Mind Mapping for Collaborative Discussion: An Exploratory Qualitative Classroom Study Using Personary
Generative artificial intelligence (AI) is increasingly shaping how university students search for information, write, create, communicate, and solve problems. In higher education, this situation requires not only operational skills for using AI tools, but also broader competencies such as critical evaluation, information literacy, ethical judgment, self-regulation, and collaborative reflection. This study examines a classroom practice using Personary, a digital mind-mapping platform with an optional AI-assisted mode, to explore how university students conceptualize competencies needed in the AI era. The activity was conducted at two Japanese universities. Students received a common instructional presentation on digital safety, misinformation, AI risks and benefits, cognitive bias, and digital well-being. They then discussed the question, “What competencies should university students develop in the AI era?” and created collaborative mind maps using Personary. Student-generated mind maps and written reflections were analyzed through interpretive map analysis and text-mining-assisted qualitative analysis. The results show that students understood AI-era competencies as multidimensional capacities rather than as technical skills alone. Their maps and reflections emphasized critical evaluation of AI-generated information, media and data literacy, autonomous thinking, communication, ethical responsibility, appropriate AI use, and adaptability. Personary supported the externalization and organization of these ideas, while the AI-assisted mode provided additional prompts for expanding selected branches. The study demonstrates how AI-supported mind mapping can function as a reflective learning activity for visualizing, sharing, and reorganizing students’ understanding of AI literacy in higher education.
Hiroko Kanoh
Open Access
Article
Conference Proceedings
Trust in AI Revisited – The Enduring Role of Propensity to Trust
The importance of trust in artificial intelligence (AI) continues to grow, as trust is widely regarded as a critical prerequisite for organizational AI adoption. In this context, intention to use AI can be understood as a consequence of the decision to trust AI and is therefore strongly influenced by trust. Moreover, trust is regarded as essential for understanding the impact of increasing interaction with AI systems on both individuals and society. Much of the discussion on trust in AI relies on frameworks derived from trust in automation, but these approaches remain largely theoretical and insufficiently validated. One important empirical contribution addressing this gap is the path model developed by Karg, Ritz and Asprion (2025), which examined trust in ChatGPT using a student sample. This model conceptualizes perceived trustworthiness through performance, process, and purpose. Together with a user’s propensity to trust, these factors are assumed to determine trust in AI. Karg, Ritz and Asprion (2025) demonstrated that perceived trustworthiness is significantly shaped by users’ inherent propensity to trust, in turn, influences the intention to use AI. The present study replicates this path model using a business sample to assess the robustness of the original findings and to advance theory building. An online survey was conducted among 97 employees of a major Swiss bank, employing identical items and methodologies as in the original study. The replication largely supports the original findings. However, in contrast to the original study, performance did not significantly predict trust in AI in the business sample. The findings further reinforce the argument that users’ dispositional characteristics may play a more decisive role in shaping perceived trustworthiness of and trust in AI systems.
Jona Karg, Janine Jäger, Petra Maria Asprion
Open Access
Article
Conference Proceedings
Rapid Judgment in the Age of AI: A Thin-Slice Study of Virtual Humans to Support Caregivers
Caregivers often face barriers to accessing timely and sustained support, motivating the development of scalable digital interventions such as virtual humans. As exposure to artificial intelligence (AI) systems becomes increasingly widespread, users may bring different expectations to their initial encounters with such systems. This is particularly consequential in caregiving contexts, where first impressions can shape trust, perceived appropriateness, and willingness to engage with supportive digital interventions. This study examines how individuals evaluate a caregiver-support virtual human under thin-slice conditions, based on a 10-second exposure. Participants (N = 354) viewed a brief video and provided open-ended impressions, which were analyzed using inductive thematic coding and aggregated into higher-level constructs. Responses were compared across groups defined by prior AI exposure. Results indicate that capability-related concerns were significantly more prevalent among participants with no exposure, whereas relational concerns increased among those with prior use. These findings suggest that initial evaluations vary systematically with prior experience, with implications for designing caregiver-facing systems as AI exposure increases.
Sharon Mozgai, Lila Rabinovich, Caroline P Nguyen, Katie Seymour, Sujeet Rao, Todd Keitz, Marco Angrisani
Open Access
Article
Conference Proceedings
Investigating Sharing Intentions for Online Public Health Education Games
Insomnia has become an important public health issue. In addition to affecting people’s physical mental health and quality of life, it has also increased the problem of sedative and hypnotic isuse. Recent studies have shown that the number of sedative and hypnotic users in Taiwan continues to increase, while the proportion of non-medical use (NMU) among adolescents and young females has also demonstrated an upward trend. The overall usage rate of hypnotics and NMU both show signs of continuous growth, highlighting the importance of Health Education regarding the proper use of hypnotics. However, most current Health Education campaigns still rely on one-way information dissemination, it lacks interactivity and entertainment, making it difficult to encourage the public to actively engage with and share health information. As Social Platforms have gradually become important channels for health information dissemination, how to enable the public to access Health Education knowledge through more attractive approaches has become an important issue.Therefore, this study used the online serious game The Nap Master’s Odyssey, developed by the Food and Drug Administration of the Ministry of Health and Welfare in Taiwan, as a case study to explore the relationships among Perceived Usefulness, Perceived Enjoyment, and Sharing Intentions among users of different genders after experiencing the game. The Nap Master’s Odyssey combines Health Education content with gamification design through narrative scenarios, interactive quizzes, and instant feedback to guide users in learning knowledge related to the proper use of sedative and hypnotic medications. In addition, the game ending interface includes personalized guardian characters, sharing interfaces, and recommended medication information, allowing players to receive personalized feedback results. This study collected data through an online questionnaire survey. Participants were required to experience The Nap Master’s Odyssey before completing the questionnaire. A total of 202 questionnaires were collected, of which 196 were valid. The results showed that both Perceived Usefulness and Perceived Enjoyment had significant positive effects on Sharing Intentions. This indicates that when users perceive the game content as practically useful and entertaining, they are more willing to share the game results on Social Platforms. In addition, the study found significant gender differences in Perceived Enjoyment and Sharing Intentions, with males scoring significantly higher than females, while no significant gender difference was found in Perceived Usefulness. These findings suggest that males may prefer game-based approaches to support learning in Health Education contexts.The main contribution of this study is confirming that serious games can serve not only as Health Education tools but also as effective health communication media when combined with personalized result presentation, character-based design, and sharing functions. In particular, the personalized guardian characters and result feedback design may not only increase the entertainment value of the game, but also encourage players to share their game results on Social Platforms, thereby improving the reach of Health Education games and enhancing the effectiveness of health promotion campaigns. The findings of this study may serve as a reference for the future design of digital Health Education games and health information communication strategies.
Tsung Lin Yang, Wenhue Chou
Open Access
Article
Conference Proceedings
In-depth Understanding of the Life Cycle of Users' Search Behaviour and their Cognitive Journey when Installing Apps
Understanding what motivates people to download apps is essential, as it influences the benefits these technologies offer and helps users find apps that fit their needs. By gaining better insights into user behaviour, we can enhance devices, services, and applications. Earlier work showed that the depth of available information influences the time and cognitive effort people spend searching for information to make an informed decision. When a user begins a search, they do not weigh all information equally. They rely on specific "entry point" cues to serve as a gateway. If an app fails these initial checks, the user will not expend the cognitive effort to look any further. In this work, we conducted an observational lab study and semi-structured interviews to investigate the cognitive heuristics and sequential behavioural pathways users employ when navigating app store environments, the importance of information cues presented by app stores, and the relationship between them across the app store stages.Using data mining approaches and path analysis, combined with theoretical frameworks of users' search behaviour, our research mapped the entire lifecycle of a user's app search and their cognitive journey from the moment they hit "search" to the final installation (e.g., Pre-Click to Post-Click).
Adel Alhejaili, James Blustein
Open Access
Article
Conference Proceedings
Sociotechnical Challenges in Remote Patient Monitoring: A Multi Stakeholder Analysis
Remote patient monitoring (RPM) is now central to hybrid and distributed care, yet its scalability remains limited by persistent sociotechnical misalignments. Despite evidence of improved deterioration detection, reduced readmissions, and better chronic disease control, adoption is uneven and many programs stall at the pilot stage. This study uses ontology informed analysis, drawing on sociotechnical systems theory, cognitive ergonomics, digital health governance, and a review of more than 200 vendors, to identify eight recurring archetypes with distinct workflows, incentives, and regulatory exposures. Across these models, interoperability gaps, cognitive load, workflow fragmentation, and incentive misalignment emerge as dominant barriers, shaping patient trust and engagement, clinician workload, and organizational integration and compliance costs. Overall, RPM effectiveness is strongly dependent on alignment across human, technical, organizational, and policy actors, making resolution of these sociotechnical constraints essential for scalable, equitable, person centered remote care.
Nabil Badr
Open Access
Article
Conference Proceedings
Bridging Language Diversity through HCI and UX: Enhancing Usability and Experience in Multilingual Web Interfaces
Human Computer Interaction (HCI) and User Experience (UX) serve as critical foundations for designing digital interfaces that are intuitive, inclusive, and user-centered. In multilingual digital landscapes, where users interact across diverse languages, scripts, and cultural frameworks, effective UX becomes essential to bridging linguistic diversity and strengthening equitable web accessibility. This paper explores how HCI and UX principles can be strategically applied to enhance usability, user satisfaction, and trust in multilingual web interfaces, enabling seamless interaction for global and linguistically varied audiences.Multilingual UX directly influences how users perceive and navigate web environments and significantly contributes to reducing cognitive load, improving readability, and supporting meaningful engagement. As research demonstrates, users prefer interfaces in their native languages, and localized experiences increase digital adoption, comprehension, and retention. However, designing multilingual web experiences introduces complex challenges, including maintaining structural and functional consistency across language versions, managing text expansion and contraction during translation, and accommodating script-specific rendering and typographic requirements. For languages with Right-to-Left orientation, such as Arabic and Hebrew, interface mirroring and rearrangement of layout components demand further HCI-driven adaptations.Cultural diversity further shapes user expectations, interpretations, and emotional responses to digital interfaces. Colors, symbols, images, metaphors, tone, and interaction patterns vary dramatically across cultural contexts, and insensitive design choices can lead to misunderstanding or loss of credibility. HCI frameworks emphasize the need for culturally responsive design, ensuring that interaction behaviors and visual cues align with local norms.Additionally, typography and script management are crucial for readability and accessibility. Complex scripts such as Devanagari, Bengali, Thai, and Arabic require specialized fonts with robust ligature support and Unicode compliance to avoid display inconsistencies and accessibility barriers. Equally important is translation quality poor or literal translations compromise clarity, functionality, and user trust. Effective multilingual design therefore necessitates comprehensive localization workflows, translation memory tools, terminology glossaries, and context-aware review processes.This paper identifies essential elements of strong multilingual UX, including intuitive usability, WCAG-aligned accessibility standards, adaptive and responsive interface systems, optimized performance, context-appropriate interactions, seamless language switching mechanisms, culturally aware content structuring, and user-centric testing tailored to linguistic communities. The integration of data-driven analytics and iterative HCI research further supports evidence-based improvements.By synthesizing HCI methodologies with multilingual UX strategies, organizations can design digital interfaces that transcend language barriers and enable inclusive participation in global digital spaces. In conclusion, advancing multilingual web usability is vital for fostering social, cultural, and economic inclusion. Applying rigorous UX and HCI principles ensures equitable access, strengthens user trust, and enhances overall digital engagement across diverse linguistic ecosystems.
Anil Gupta, Kajal Pandey
Open Access
Article
Conference Proceedings
Toward Culturally Sensitive Emotion Estimation through Multimodal Sensing and Everyday Behavioral Cues
This paper aims to provide new insights for the advancement of emotion estimation technologies through a comprehensive analysis that integrates model comparison, behavioral correlation analysis, and a multicultural perspective. First, we conduct a comparative review of representative emotion estimation models, including Russell’s two-dimensional circumplex model and Plutchik’s three-dimensional emotion model. Their theoretical foundations, expressive capacities, and applicability to computational emotion estimation are examined to clarify their respective strengths and limitations in practical implementations.Based on this theoretical comparison, we construct an emotion estimation model using physiological and behavioral sensing. Heart rate data acquired from a heart rate sensor are combined with pressure data obtained from pressure sensors embedded in the environment. In addition to these sensor signals, behavioral features such as the degree of leaning against a chair and the timing of beverage consumption are extracted as observable indicators of user behavior. These multimodal data are integrated to estimate users’ emotional states, enabling an analysis that goes beyond physiological signals alone.We then investigate the relationship between estimated emotional states and behavioral data in detail. Statistical analyses reveal that specific behavioral patterns exhibit meaningful correlations with particular emotional dimensions, suggesting that behavioral data can serve as complementary information for emotion estimation. The results indicate that incorporating behavior-based features can enhance robustness and interpretability, especially in situations where physiological signals are noisy or insufficient.Furthermore, this study examines emotion estimation from a multicultural perspective by considering differences in emotional expression across cultural backgrounds. We analyze how cultural factors influence both observable behaviors and physiological responses, and how these differences affect the performance of emotion estimation models. The findings demonstrate that models developed within a single cultural context may not generalize well to others without adaptation, highlighting the necessity of incorporating cultural sensitivity into model design and evaluation.Overall, this research contributes to the field of affective computing by demonstrating the value of combining established emotion models with multimodal sensing and behavioral analysis while explicitly addressing cultural diversity. The proposed approach provides a foundational framework for improving the accuracy and applicability of emotion estimation systems in real-world settings. By integrating theoretical model comparison, empirical sensor-based analysis, and multicultural considerations, this study broadens the scope of emotion estimation research and supports the development of more inclusive and reliable emotion-aware technologies for future applications.In addition, the implications of this work extend to human–computer interaction, social robotics, and adaptive systems that respond to users’ internal states. By leveraging everyday behaviors that naturally occur during interaction, the proposed framework reduces reliance on intrusive measurements and supports more seamless deployment in daily environments. The methodological insights presented in this paper can inform the design of future studies, including longitudinal experiments and real-time adaptive systems. Ultimately, this research underscores the importance of interdisciplinary approaches that integrate psychology, sensing technology, and cultural studies, and it lays the groundwork for scalable emotion estimation systems capable of supporting personalized and context-aware interactions across diverse user populations.These contributions collectively advance emotion-aware system design for ethically responsible global technological innovation.
Meina Tawaki, Ichi Kanaya, Munenori Koyasu, Keiko Yamamoto
Open Access
Article
Conference Proceedings
PathFinder: Haptic Wrist Guidance for Ambiguous Pedestrian Routes
Pedestrian navigation systems commonly rely on visual guidance, but such guidance may divide attention during walking. Haptic guidance has been proposed as an eyes-free alternative, yet its effectiveness across different outdoor environments remains unclear. We report a within-subjects field study comparing minimal visual arrow guidance and wrist-worn haptic guidance across urban and non-urban pedestrian routes. Fourteen participants completed navigation tasks in both modalities and both environments using a custom Android system integrating smartphone GPS/GNSS and compass input with Bluetooth-connected wrist-worn haptic devices. Performance was assessed using average speed, route deviation, and completion time; subjective measures included the NASA-TLX and a haptic experience questionnaire. Data were analyzed using 2 × 2 repeated-measures aligned rank transform (ART) ANOVA, testing the main effects of Mode, Environment, and their interaction. Environment had a strong effect on average speed, and a significant Mode × Environment interaction indicated that modality differences depended on environmental context. Visual guidance produced lower route deviation and shorter completion times overall, whereas haptic guidance was associated with higher mental demand. Haptic experience ratings did not differ significantly across environments. These findings suggest that pedestrian navigation performance is shaped by both interface modality and environmental context, and that the benefits of haptic guidance are not uniform across settings. The results highlight the importance of ecologically valid, context-sensitive evaluation of navigation interfaces.
Richa Singh, Mohit Nayak, Ahmed Farooq, Jakub tkacz, Mounia Ziat, Roope Raisamo
Open Access
Article
Conference Proceedings
Analyzing the Viscoelastic Correspondence Between Facial Electromyography and Facial Appearance During Smiling
Quantitative modeling of facial expression dynamics is fundamental for human-centered sensing and interaction. While facial appearance provides a non-contact means of observation, assigning consistent intensity values remains challenging. Conversely, facial electromyography (fEMG) directly captures muscle activation but requires physical contact. This study investigates the dynamic correspondence between these modalities during smiling by evaluating whether an interpretable viscoelastic model can characterize their relationship. To overcome the challenge of fEMG electrodes occluding facial features, we utilized the bilateral symmetry of facial expressions, recording fEMG on one side of the face while capturing video of the contralateral side. Facial appearance was quantified using a comparison-based ranking method to derive ordinal smile intensities, while fEMG signals were decomposed into muscle synergies using non-negative matrix factorization. We hypothesized that the transformation from muscle contraction to visible tissue deformation follows viscoelastic principles. Specifically, we employed the standard linear solid (SLS) model to account for temporal dynamics and compared its performance against a static linear baseline. Experimental results from natural smiles elicited by comedic stimuli revealed strong correlations between synergy activation and smile intensity, with facial appearance consistently lagging behind muscle activity. The SLS model outperformed the linear baseline for the majority of participants, more accurately capturing delayed onsets and gradual relaxation phases. These findings suggest that incorporating viscosity provides a physically interpretable explanation of expression manifestation. This framework establishes an explainable bridge between physiological activity and visual dynamics, offering a foundation for more robust facial sensing technologies.
Kei Shimonishi, Takuma Kogo, Kazuaki Kondo, Hirotada Ueda, Yuichi Nakamura
Open Access
Article
Conference Proceedings
Past and future human-machine interfaces – a matter of present concern?
Human-machine interaction has become a central part of life in modern societies. Facilitating labour, assisting with repetitive tasks, and creating significant gains of time and economic factors, computer assisted life is widely accepted as a necessity.However, with the proliferation of computers and artificial intelligence around humans, drawbacks and unexpected negative effects have been rising. Knowledge extinction, false resilience as well as induced complacency have become a serious concern in the transportation industry (specifically aviation and automotive industry). This research focuses on pointing out the direction taken by the industry, the danger of long-term exaggerated usage of automation, and the compromises that should be made to step-down from a pathway that seems hazardous for humanity.
Yann Kowalczuk, Jan Holub
Open Access
Article
Conference Proceedings
User Experience Assessment for Smart Products in IoT Environments: A Systematic Review
Smart products are increasingly embedded in everyday environments. Unlike traditional interactive systems, they operate across multiple touchpoints, involve multiple users simultaneously, and evolve over time through software updates, adaptive algorithms, and changing ecosystems. As a result, UX with smart products is shaped not only by task performance and usability but also by long-term processes of appropriation, agency calibration, and carry specific associated values and concerns. Despite this complexity, it remains unclear how current UX assessment methods account for these characteristics.This paper presents a systematic literature review investigating how UX has been evaluated in the context of smart and IoT products. Following PRISMA 2020 guidelines, 44 studies covering the last decade were selected from Scopus, ACM Digital Library, and Web of Science. The review identifies a persistent mismatch between the complexity of smart product experience and dominant evaluation practices. The contribution aims, on one side, at extracting methodological directions and, on the other, at conceptual reframing to shift evaluation practice: moving beyond the assessment of mere usability, and considering agency and control dynamics, as well as multi-user and multi-touchpoint dimensions.
Agnese Azzola, Venanzio Arquilla, Lucia Rampino
Open Access
Article
Conference Proceedings
Framework to Support SMEs Transition from Traditional to Connected Smart Medical Devices: Value Creation Through Data and Enhanced User Experience
The medical device industry is undergoing a transformative shift, driven by the integration of connectivity, data analytics, and user-centered design. While large corporations are increasingly adopting smart and embedded medical systems, small and medium-sized enterprises (SMEs) often face significant barriers in this transition due to limited resources, limited hardware-software co-design expertise, fragmented knowledge, and regulatory complexity. This paper presents the design of a new comprehensive framework adapted to support SMEs in evolving from traditional standalone medical devices to connected Internet of Things (IoT)-enabled embedded medical device systems. The proposed framework integrates technological, organizational, and human-centered dimensions to guide SMEs through critical stages of transformation, including digital capability assessment, data strategy and interoperability across ecosystems, and stakeholder experience optimization. By enabling SMEs to harness the full value of data generated by connected embedded devices, the framework supports evidence-based decision-making, predictive maintenance, personalized healthcare solutions, and enhanced collaboration across the medical ecosystem. Further, emphasizing the co-creation value among diverse stakeholders including patients, clinicians, manufacturers, and regulators through improved user experience and data-driven insights. An iterative design approach combines literature analysis, expert consultations, and case study validation within the medical device industry. Results demonstrated potential to accelerate digital transformation, reduce innovation risks with embedded systems, and foster sustainable competitiveness among SMEs in the emerging smart healthcare landscape.
Omar Huerta, Dipo Olaosun, Jonathan Biner, Tauqeer Ali Khan, Peter Culmer
Open Access
Article
Conference Proceedings
FADA: An AI-Enabled Healthcare Informatics Platform for Prenatal Ultrasound Analysis, Clinical Decision Support, and Automated Reporting
Prenatal ultrasound examination is the primary screening tool for monitoring fetal development and identifying hereditary abnormalities before birth. Despite its widespread use, accurately interpreting fetal brain ultrasound images requires specialized expertise and considerable clinical experience. Variations in operator skill, image quality, reporting practices, and access to specialist services can influence diagnostic consistency and timeliness. These challenges highlight the need for decision-support tools that can assist clinicians in image interpretation while fitting naturally into existing clinical workflows.This paper presents the Fetal Abnormality Detection Application (FADA), an AI-enabled healthcare informatics platform designed to support fetal brain ultrasound assessment through automated image analysis, clinical decision support, interactive consultation, and report generation. The platform provides an integrated environment where healthcare professionals can upload ultrasound images, receive automated measurements and abnormality assessments, interact with an intelligent clinical assistant, and obtain structured diagnostic reports through a unified interface.At the core of FADA is a curated dataset containing more than 3,000 expert-annotated fetal brain ultrasound images. The dataset focuses on clinically important anatomical structures, including the Cavum Septum Pellucidum (CSP) and Lateral Ventricles (LV), both of which serve as important indicators of fetal neurological development. The annotated dataset forms the basis for machine learning models trained to identify relevant anatomical regions, perform measurements, and detect patterns associated with developmental abnormalities. The platform supports automated assessment of these structures and produces quantitative measurements that can assist clinicians during prenatal evaluation.The system architecture follows a modular healthcare informatics design consisting of four primary functions: user interaction, clinical data processing, intelligent analysis, and results management. Healthcare professionals interact with the platform through a web-based clinical workspace that supports image submission, consultation, and review of findings. Submitted images are processed through an AI analysis pipeline that performs anatomical identification, measurement extraction, abnormality assessment, and generation of preliminary findings. In parallel, an intelligent conversational assistant enables users to ask questions regarding imaging findings, measurements, and clinical interpretation, providing contextual guidance and explanatory information. The outputs of these services are consolidated into structured clinical summaries and downloadable reports suitable for review and incorporation into existing clinical documentation workflows.A distinguishing aspect of FADA is the integration of image interpretation and clinical reporting within a single workflow. Rather than functioning solely as a diagnostic algorithm, the platform acts as a healthcare information system that connects image acquisition, automated analysis, clinician interaction, and report generation. This approach reduces repetitive administrative tasks, promotes reporting consistency, and supports traceable documentation of imaging findings.The proposed platform demonstrates how artificial intelligence can be embedded within healthcare information systems to support routine prenatal screening and fetal assessment. By combining expert-annotated imaging data, automated anomaly detection, conversational clinical assistance, measurement extraction, and report generation, FADA provides a practical model for computer-assisted prenatal diagnostics. The platform illustrates the potential of healthcare informatics to improve the efficiency, consistency, and accessibility of fetal ultrasound assessment while preserving clinician oversight in the diagnostic process.
Zain Tariq, Mahmood Al Zubaidi, Marco Agus, Mowafa Househ
Open Access
Article
Conference Proceedings
Development of the Rob’Tales methodology to teach emotional an social skills to autistic adolescents
Autistic adolescents frequently experience difficulties with emotional and social skills. Robots are promising tools to promote the development of those abilities. This study aimed to develop a robot-assisted therapy named Rob’Tales, targeting social and emotional skills in autistic adolescents. Methods: Intervention development was based on three complementary sources of information: (1) a scoping review to identify didactic techniques used in evidence-based practices and potential intervention targets, (2) a feasibility study to refine the intervention structure, (3) autistic individuals opinions to define the curriculum. Results: Two interventions considered as EBP were identified: Social Skills Training and Cognitive Behaviroral/Instructational Strategies. Common modules taught in SST were listed. These results were used to create a first version of Rob’Tales, implemented in a feasibility study whose results inform about the strengths and shortcomings of this first version. Preference ratings showed that modules related to psychoeducation, emotion, and interpersonal problem solving should be prioritized. Conclusion: This paper presents the construction of Rob’Tales, built from three different sources of information. Its efficiency promoting emotional and social well-being of autistic adolescents should be tested.
Margot Dumas, Eric Meyer, Sophie Sakka
Open Access
Article
Conference Proceedings
AI Guided Simulated Annealing for Automated Gene Editing Design
Designing effective gene and mRNA sequences is a difficult optimisation problem because the number of possible nucleotide combinations grows extremely quickly with sequence length. Traditional optimisation methods such as simulated annealing are well suited to exploring these large search spaces, but their performance depends heavily on the quality of the scoring function used to evaluate candidate sequences. Hand crafted scoring rules are often slow to compute and cannot easily adapt to different biological contexts or patient specific constraints. This project presents an AI-guided simulated annealing framework for automated gene sequence design using two approaches. The first replaces fixed rule-based scoring with an adaptive model evaluating candidates using biological reference data and patient-specific information. By adjusting biological trait importance based on age, disease background, and treatment goals, the scoring model dynamically changes sequence evaluation without modifying the optimization algorithm. The second approach employs Gradient Boosting Regression on CRISPR guide RNA sequences with extracted biological features including GC content, positional nucleotides, and sequence complexity metrics. This model learns from validated literature guides, providing interpretable, deterministic scoring while maintaining adaptability. The framework is designed to support long running and repeated simulated annealing searches with minimal human intervention. Sequence evaluation is decoupled from the optimisation engine so that scoring models and reference databases can be updated as new experimental or clinical data becomes available. This allows the same optimisation pipeline to be reused across different applications such as vaccine design, cancer related gene targets or personalised therapies. By combining a fast native optimisation core with an adaptive and context aware evaluation model, this work demonstrates a flexible approach to large scale gene sequence optimisation. The proposed system highlights how AI driven scoring can improve the practicality of heuristic search methods and move sequence design closer to personalised and data driven biomedical applications.
Dai Duong Nguyen, Ryan Shaw, Kenneth Y T Lim
Open Access
Article
Conference Proceedings
Human-Centered Information Design for Toxicological Risk Communication among Domestic Workers
Domestic workers are frequently exposed to chemical hazards during routine cleaning activities, particularly through the mixing of commonly used household cleaning products such as chlorine-based bleach, ammonia, and acidic agents. These combinations can produce toxic gases and harmful chemical reactions that pose significant risks to respiratory and overall health. Despite these dangers, information about chemical incompatibilities is often fragmented, highly technical, or poorly adapted to the everyday working conditions of domestic workers. This study investigates how human-centered information design can support safer cleaning practices by improving the communication of toxicological risks associated with household cleaning products. The research was conducted with domestic workers in the metropolitan area of Monterrey, Nuevo León, Mexico, where domestic work represents a significant yet frequently informal labor sector. The study follows a user-centered design process consisting of three phases: contextual inquiry, information design, and user evaluation. Semi-structured interviews and exploratory inquiries were first conducted to identify commonly used cleaning products and existing practices related to product mixing. Based on these findings, a printed visual communication tool was developed to communicate safe and unsafe product combinations using principles of information design and visual risk communication. The tool was then evaluated through task-based usability tests and a questionnaire to assess comprehension, usability, and perceived usefulness. The findings highlight how human-centered information design can enhance risk comprehension and support safer practices in domestic cleaning tasks. The study contributes to research on occupational safety and human-centered design by demonstrating how visual risk communication strategies can improve toxicological safety awareness among workers in informal labor contexts.
Mariel Garcia-Hernandez, Fabiola Cortes-Chavez, Alberto Rossa-Sierra, Gabriela Duran-Aguilar
Open Access
Article
Conference Proceedings
Systems-of-Systems: Engineering or Construct?
The concept of Systems-of-Systems (SoS) has become increasingly important across domains such as engineering, defence, healthcare, and digital infrastructure, describing complex assemblies of independent systems that collectively deliver capabilities beyond individual components. However, the concept remains debated: are SoS objective engineering entities or “constructs” used to interpret socio-technical complexity? Having its origins in the context of systems engineering and defence acquisition, SoS are defined by its operational and managerial independence, evolutionary development, and emergent behaviour. While these traits distinguish SoS from supersystems, they also reflect how complexity is framed. Two main perspectives are examined. The engineering perspective treats SoS as designable and controllable entities, emphasizing architecture, standards, governance, and lifecycle management approaches. Here, the challenge is extending traditional engineering methods to address scale, decentralization, and emergence. In contrast, the constructivist perspective sees SoS as interpretive frameworks for understanding loosely coupled, evolving systems shaped by organizational and social contexts. In this view, system boundaries, purposes, and identities are fluid and negotiated. Each perspective has implications. Engineering approaches support structure and measurable performance but may overlook human and political dynamics. Constructivist approaches highlight power, incentives, and adaptation but offer less practical design guidance. The paper argues that real-world SoS embody both views, combining engineered structures with emergent, socially shaped dynamics. It proposes a synthesized perspective that understands SoS as socio-technical phenomena—partly designed and partly constructed. This dual view suggests that effective SoS design and management require integrating engineering practices with insights from organizational theory, complexity science, and systems thinking.
Pedro Água, Anacleto Correia, António Gonçalves
Open Access
Article
Conference Proceedings
Prevention of Collisions Between Mobile Machinery and Pedestrian Workers: A Review of Proximity Detection Technologies
The risk of collisions between mobile industrial equipment and pedestrian workers represents a major occupational health and safety concern, particularly on construction sites. The use of driver assistance systems, such as proximity detection devices designed to warn workers and equipment operators of potential collision hazards, is an increasingly explored and rapidly evolving prevention strategy. This article proposes a review of the various proximity detection technologies available for managing these collision risks on construction sites, and through consultation of suppliers, portrays the key market challenges, product characteristics, customer needs, installation-related issues, as well as the strengths and limitations of these different technologies. The results highlight the technical, human, and organizational complexity associated with the selection and implementation of proximity detection technologies. Successful deployment relies not only on technological performance, but also on user acceptance, adaptability to real work contexts, and the ability of these systems to support operators’ vigilance without overloading it.
François Gauthier, Damien Burlet-Vienney, Chantal Gauvin, Aida Haghighi
Open Access
Article
Conference Proceedings
Human Factors Challenges in Future Remote Operations for Electrified Short-Sea Ro-Ro Ferries
Maritime operations are undergoing significant transformation driven by increasing levels of autonomy, electrification, digitalization, and artificial intelligence. These trends, combined with crew shortages are accelerating interest in Remote Operation Centers (ROCs) for shore-based fleet supervision. This is particularly relevant for short-sea Ro-Ro ferry operations, where repetitive, constrained, and time-critical electrified routes make ROC operations a promising approach for improving safety, performance, and energy efficiency. However, ROC implementation is not merely a technical change — performance, safety, and scalability will depend heavily on how human factors challenges are understood and addressed. Existing research has identified key issues such as insufficient situational awareness, vigilance decline, workload dynamics, trust, and communication challenges. Yet prior work has largely focused on navigation-oriented supervision, with limited attention to engineering tasks, and much of the literature remains conceptual or simulation-based. Current research therefore provides only a partial view of human factors challenges within the broader sociotechnical reconfiguration of roles and human-technology interaction across ship and shore. This paper addresses this gap through an exploratory qualitative study combining field visits, observations, and interviews across onboard and real-world ROC environments for Nordic short-sea ferry operations. Data from more than 40 participants including master mariners, ship engineers, and maritime experts were analyzed using thematic analysis supported by MAXQDA and affinity diagramming. The findings reveal interconnected human factors challenges spanning cognitive, organizational, physical, and technical-system domains. Key challenges include loss of embodied navigational and engineering cues, high switching demands in multi-vessel supervision, cognitive fatigue from multi-screen monitoring, role ambiguity, and intensive cross-organizational communication. These are compounded by fragmented interfaces, alarm floods, and unreliable connectivity, increasing operator burden in time-critical operations. The findings extend prior research by providing an empirically grounded view across both navigational and engineering domains, with implications for system design, operator competencies, organizational arrangements, and human-centered ROC development.
Yemao Man, Anders Persson
Open Access
Article
Conference Proceedings
Machine Learning-Based Prediction of Cybersecurity Vulnerability Severity for Enterprise Security Management
Cybersecurity vulnerabilities represent a growing challenge for modern information systems, particularly in large-scale cloud and enterprise environments where organizations must process a high volume of vulnerability disclosures under strict time constraints. Effective prioritization of security updates is essential for minimizing operational risk and improving resource allocation within Security Operations Centers (SOCs). However, traditional vulnerability severity assessment methods, such as the Common Vulnerability Scoring System (CVSS), rely heavily on manual analysis and expert judgment, limiting scalability in dynamic environments. This paper presents a machine learning–based framework for predicting the severity of cybersecurity vulnerabilities using structured data derived from Microsoft Security Bulletins. The dataset spans more than 15 years (2001–2017) and contains over 23,000 vulnerability records characterized by attributes such as impact type, affected product, affected component, and associated CVE identifiers. The prediction task is formulated as a multi-class classification problem with four severity levels: Critical, Important, Moderate, and Low. Several supervised learning models, including Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting, are evaluated using macro-averaged precision, recall, and F1-score. Experimental results demonstrate that ensemble learning methods significantly outperform baseline classifiers. In particular, the Gradient Boosting model achieves the best performance, with a macro-F1 score of 0.982 and an overall accuracy of 99.0%. The findings demonstrate the effectiveness of machine learning techniques for automated vulnerability prioritization and highlight their practical applicability in enterprise cybersecurity management and SOC environments.
Adnan Agbaria, Iyad Suleiman
Open Access
Article
Conference Proceedings
Cognitive training for adults with developmental deficits led by a socially intelligent robot
This work presents a socially intelligent robotic system for cognitive training of adults with developmental deficits in a therapist-half-supervised setting. The core idea is to move beyond one-way robot prompting toward a structured, ethically grounded, data-aware intervention framework in which the robot, end user, and therapist form a coordinated triad. The proposed system combines robot behaviour management, session orchestration, user monitoring, and therapist oversight within a unified architecture suitable for iterative real-world deployment.The benefit of this approach lies in the effective integration of artificial intelligence components, human-robot interaction, psychology, and the extensive experience of human experts. Interaction in a real-world environment and the labeling of test datasets occur simultaneously. The contribution is fourfold. First, we define a computer-robot-therapist-end-user architecture that explicitly models information flow, therapist control, and adaptive interaction. Second, we provide an experimental procedure for cognitive training sessions that includes training content, session organization, and a user-experience evaluation framework. Third, we introduce a data protection and ethics plan tailored to vulnerable populations, emphasizing informed participation, controlled data access, and responsible system adaptation. Fourth, we outline an approach for objective estimation of the end user’s cognitive state, with the longer-term goal of enabling robot behaviour that is responsive to fatigue, attention, overload, or engagement. Preliminary results indicate that socially guided robot-based training is feasible and acceptable within the proposed framework. A central ongoing challenge is to identify meaningful robot-user interaction events in the relation to their contexts (situations) and to map them to suitable user and robot actions. We therefore treat interaction-event modelling not as a secondary implementation detail but as a foundational design layer for adaptive socially assistive robotics. The longer-term significance of this work lies in connecting clinical and social needs with computational interaction design, making robot-led training more personalized, transparent, and operationally safe for adults with developmental deficits.
Andrej Košir, Urban Burnik, Janez Zaletelj, Gaja Gril, Ajda Svetelsek, Sašo Tomažič, Anja Podlesek
Open Access
Article
Conference Proceedings
Complex Problem Solving: From Ambiguity to Solution Design
With the fast growing spread of artificial intelligence (AI), everyone with access to it is increasingly using it. Despite the advantages of using AI in terms of efficiency, there is some downside to such dynamics. Complex problems remain complex for a reason, and such suggests a call into practical wisdom and complex problem-solving skills. Moreover technology is triggering cognitive decline for some extent, which also adds to the need to keep what makes us humans in terms of decision making in complex contexts. This paper presents an integrated framework for strategic problem solving based on problem definition, goal setting, Ishikawa diagrams, problem trees, and solution trees. The framework is designed to support decision-makers facing complex and uncertain environments – the new normal. Drawing on systems thinking, critical thinking, the Cynefin framework, root-cause analysis, and organizational change theory, the paper presents a structured methodology rooted on the Theory of Constraints for moving from symptoms to root causes and from desirable outcomes to solution design. A well known strategic management case study is used as a pedagogical example to illustrate how strategic challenges can be framed, analysed, and resolved. The paper argues that effective strategic problem-solving skills requires not only analytical rigor but also stakeholder engagement, awareness of cognitive biases, before disciplined implementation. The proposed framework is particularly relevant for higher education, management practice, public administration, and innovation-oriented organizations, where a VUCA world challenges decision makers, managers, and leaders, or educators on their daily jobs.
Pedro Água
Open Access
Article
Conference Proceedings
Legislative Support System to Assist in the Transfer of Skills for Municipal System Design and Ordinance Drafting
This paper presents a legislative support system designed to facilitate the transfer of drafting skills. This system called "eLen Ordinance Database System", supports the works in ordinance drafting by providing access to a comprehensive database of approximately 1.4 million Japanese ordinances. Publicly available free since 2012, the system has been integrated into the daily routines of numerous municipal officials, who actively use the specific functions described in this paper. Beyond standard database queries, the system automatically generates ordinance comparison tables and templates for supporting legislative drafting. These features help the drafters adopt established drafting practices from existing ordinances and explore alternative administrative system designs. According our results of interviews with local government officials it is indicated that ordinance drafting prioritizes policy intent and strict adherence to conventional notation, typically building upon practices established by predecessor drafters. To address these practical needs, the system shown in the paper has used a supercomputer to evaluate similarities and dissimilarities across ordinances, thereby extracting typical patterns and unique characteristics. In practice, typicality of policies are identified within large clusters of similar ordinances, while a diverse range of policy options is uncovered by analyzing variations within those clusters.
Tokuyasu Kakuta, Daichi Saito
Open Access
Article
Conference Proceedings
Design, Implementation and Integration of a Mobile Sensor Platform for On-Site Ship Emission Measurements
Maritime transport is a major contributor to air pollution in port areas, emitting nitrogen oxides (NOx), carbondioxide (CO2), particulate matter (PM), and ozone precursors. While high-precision reference stations provide accurate data, they are limited in spatial coverage and mobility. This paper presents the design, implementation, and field evaluation of a low-cost, portable immission monitoring system tailored for harbor environments. The system integrates electrochemical, NDIR, and laser scattering sensors within a modular hardware architecture as a weather-resistant external sensor unit. A lightweight software framework enables modular sensor integration, standardized data handling, and wireless transmission to a central server. Field tests alongside a calibrated stationary measuring system demonstrate that the NO2 sensor closely matches reference measurements in medium concentration ranges, while deviations occur at low levels, partly due to the applied moving average filter. The results confirm the system’s robustness, ease of deployment, and suitability for mobile air quality monitoring in ports. Future work will address energy independent operation, improved calibration of additional sensors, and integration with ship traffic data for emission source attribution.
Thimo Florian Schindler, Simon Schlicht, Till Büchter, Jan-hendrik Ohlendorf
Open Access
Article
Conference Proceedings
Object Perception Pipeline for Industrial Disassembly: From 6D Pose to 3D Localization in Automotive Robotics
Industrial disassembly processes require robust and efficient perception systems capable of handling heterogeneous objects under real-world constraints. In automotive manufacturing and disassembly scenarios, components exhibit significant variability in geometry, size, symmetry, and placement, making it impractical to rely on a single, uniform object pose estimation strategy. At the same time, such environments impose strict requirements on the perception systems in terms of reliability, computational efficiency, and ease of deployment.This work presents an instance-based object perception pipeline with task-driven object pose estimation, developed within the SOPRANO EU project, for automated automotive door disassembly. The pipeline assumes a known set of object instances and depending on the objects’ geometric characteristics, the manipulation task requirements , the system performs (i) full 6D pose estimation, (ii) planar-constrained 3D localization via RGB-D lifting, or (iii) planar localization with structured multi-instance refinement and instance identification.The perception process consists of two stages: pose formulation selection and execution of the corresponding estimation method. Model-based 6D pose estimation using RGB data is applied to rigid objects, while RGB-D-based lifting of 2D detections is used for planar or quasi-planar elements. For structured arrangements of multiple instances of a single object type, such as screw arrays, a multi-instance matching strategy ensures consistent indexing and reduces ambiguity.The system is deployed as a modular perception service and validated in an automotive disassembly pilot. Experimental results demonstrate high accuracy across heterogeneous tasks, highlighting the benefits of aligning perception outputs with task-specific requirements.
Evangelos Sartinas, Athina Zacharia, Maria Pateraki
Open Access
Article
Conference Proceedings
Hydro Digital Twins as Emerging Technologies: An alluvial mapping of objectives across marine, coastal, and freshwater applications
Water territories (coastal zones, river corridors, marine basins, and lagoons) face compounding climate pressures, yet the digital tools designed to support their governance remain poorly understood in terms of what they actually aim to achieve. While Hydro Digital Twins (HDTs) are increasingly framed as instruments for climate adaptation and environmental stewardship, systematic analyses of their governance purpose remain scarce. This paper examines 14 HDT applications spanning marine, coastal, river-lake-fjord, aquaculture, and flooding domains, published between 2020 and 2025. Rather than cataloguing technical features, the analysis traces how application contexts translate into governance functions and, in turn, into concrete operational targets. An alluvial diagram links five application themes (left) to six governance-function categories: Forecasting, Modelling, Monitoring, and their combinations (centre)—and onward to 18 specific objectives (right). Three patterns emerge. First, monitoring, forecasting, and modelling functions overlap substantially across themes, revealing that HDTs are conceived as multi-functional instruments rather than single-purpose tools. Second, coastal applications exhibit the broadest governance scope, spanning spatial planning, erosion management, and environmental assessment simultaneously. Third, several objectives (saltwater intrusion management, biological component integration, participatory governance) appear as isolated, single-flow connections, signalling emergent but not yet consolidated research fronts. The paper argues that these governance flows expose both the promise and the fragility of current HDT architectures: broad ambition coexists with narrow implementation, and the distance from stated objective to operational deployment remains the defining challenge. Governance ambition has outpaced delivery capacity; institutional and data-governance frameworks such as F.A.I.R., C.A.R.E., and T.R.U.S.T. must develop alongside technical infrastructure if HDTs are to serve as instruments of equitable water-territory management.
Letizia Artioli, Pietro Costa, Giovanni Borga
Open Access
Article
Conference Proceedings
Validation of a UAV-Based Digital Twin for VRU Safety in Urban Environments with Simulations
Vulnerable Road Users (VRUs), including pedestrians, cyclists and micromobility users, remain exposed to high risk in urban traffic locations where buildings, parked vehicles or infrastructure create occlusions. This paper validates a UAV-based digital twin for VRU safety in urban environments using a combined field-test and simulation-supported methodology. The digital twin is designed as a temporary and rapidly deployable perception extension for connected and automated vehicles. The proposed Physical, Digital and Communication infrastructure combines a UAV-mounted camera, edge-based artificial intelligence, a roadside unit and the vehicle onboard unit. The UAV monitors a region of interest and streams video to the edge node, where VRUs are detected, tracked and projected into world coordinates using RTK-enabled georeferencing and camera pose information. Vehicle states are received through Cooperative Awareness Messages, while the edge node uses synchronized state estimation and short-horizon trajectory prediction to assess possible path conflicts and time-to-collision. When a relevant risk is identified, warning information is inserted into the vehicle perception loop through standardized V2X messages. The validation was performed through pedestrian-crossing trials at the EMT Carabanchel bus depot in Madrid and through openPASS simulations of the occluded turning geometry. The results show that the UAV-based digital twin can detect occluded pedestrians before they are available to onboard sensors and can support more progressive vehicle deceleration. The simulation comparison confirms that, in the occluded turning scenario, the digital-twin warning provides an operational advantage by enabling a more anticipatory and interpretable vehicle response than vehicle-only AEB. Future research will perform a large number of simulations to vary and explore more scenarios.
Ioannis Symeonidis, Nikolaos Angelopoulos, Maria Gkemou, Evangelos Bekiaris
Open Access
Article
Conference Proceedings
Cross-Border Insolvency as a Socio-Economic Second-Chance Mechanism in the European Union
The European Union has established a transnational framework for insolvency proceedings through Regulation (EU) 2015/848 on insolvency proceedings. The Regulation determines international jurisdiction for main insolvency proceedings primarily by reference to the debtor’s centre of main interests (COMI). For individuals, this may allow access to insolvency proceedings in a Member State other than their country of nationality or previous residence, provided that the COMI has been genuinely relocated and can be established under the criteria of the Regulation. This paper conceptualizes cross-border personal insolvency not merely as a question of jurisdiction, but as a socio-economic second-chance mechanism that may influence individual recovery trajectories and broader economic participation within the European Union.To place this contemporary function in context, the paper briefly traces the historical development of insolvency law from earlier punitive approaches toward debtors to modern systems of collective debt resolution, discharge, and financial rehabilitation. In doing so, it also considers the influence of common law and Commonwealth legal traditions on the evolution of insolvency concepts, particularly the gradual shift from creditor-driven enforcement and debtor sanctioning toward structured debt resolution, discharge mechanisms, and economic reintegration. This historical perspective highlights the transformation of insolvency law from a sanction-oriented mechanism into a legal framework that increasingly seeks to balance creditor protection with the debtor’s return to economic participation.Despite EU-level coordination, significant differences remain between national personal insolvency regimes, particularly regarding time to discharge, the treatment of secured and unsecured debts, procedural costs, access requirements, and the visibility of insolvency information in public registers. These differences may affect access to financial rehabilitation and can create incentives for over-indebted individuals to consider lawful relocation where a genuine COMI can be established. Against this backdrop, the paper addresses the central research question: to what extent can cross-border personal insolvency function as an effective and legitimate instrument for socio-economic reintegration within the European Union?Methodologically, the study adopts a comparative and conceptual approach, examining selected insolvency regimes within the EU with a focus on structural variables that are critical from an individual debtor’s perspective. These include time to discharge, treatment of residual debts, procedural accessibility, administrative burden, and the potential reputational implications associated with public insolvency registers. The analysis is complemented by a system-level perspective on how these variables may shape behavioral responses, decision-making processes, and access to legal remedies.Methodologically, the study adopts a comparative, conceptual, and historically informed approach, examining selected insolvency regimes within the EU with a focus on structural variables that are critical from an individual debtor’s perspective. These include time to discharge, treatment of residual debts, procedural accessibility, administrative burden, and the potential reputational implications associated with public insolvency registers. The analysis is complemented by a system-level perspective on how these variables may shape behavioral responses, decision-making processes, and access to legal remedies.In addition, the paper investigates the role of advisory and intermediary service models that facilitate cross-border insolvency procedures. These include legal advisers, personal insolvency practitioners, and relocation-related service providers. Such models may reduce informational complexity and improve access to lawful insolvency solutions, but they may also introduce new asymmetries in decision quality, cost exposure, and access to professional guidance. This dual perspective allows for a more nuanced assessment of cross-border insolvency as both an enabling mechanism and a source of emerging systemic challenges.The findings indicate that cross-border personal insolvency can contribute to financial recovery, reduce long-term socio-economic exclusion, and support the EU’s policy objective of promoting entrepreneurship and second-chance opportunities. However, the legitimacy and sustainability of this mechanism depend on maintaining a clear distinction between genuine COMI relocation and artificial forum shopping, addressing regulatory fragmentation, ensuring transparency, and mitigating risks associated with unequal access to professional support and strategic jurisdictional behavior.
Pascal Verbracken
Open Access
Article
Conference Proceedings
Tort-Based Claims and Legal Divergence in EU Insolvency Systems: Towards a Framework for Trust and Stability in Cross-Border Contexts
The increasing interconnection of European legal and economic systems has significantly amplified the importance of coherent cross-border insolvency regimes. While Regulation (EU) 2015/848 on insolvency proceedings coordinates jurisdiction, recognition, applicable law, and cooperation across Member States, it does not fully harmonize the substantive treatment of individual categories of claims. Among the most complex and insufficiently structured areas within this landscape is the treatment of liabilities arising from tort-based claims and other forms of unlawful or harmful conduct.This paper examines how claims resulting from unlawful or harmful conduct are classified, treated, included in insolvency proceedings, excluded from discharge, or otherwise affected under different national insolvency regimes, with a particular analytical focus on Ireland as an illustrative case within the European context. Ireland is especially relevant because its personal insolvency framework reflects both the historical influence of common law and Commonwealth-derived legal traditions and a modern statutory architecture designed to manage personal debt through regulated procedures. In this context, the paper briefly outlines the Irish procedural pathway, including the role of the Personal Insolvency Practitioner (PIP), who acts as a regulated intermediary between the debtor and creditors in procedures such as Debt Settlement Arrangements and Personal Insolvency Arrangements. This institutional role illustrates how Irish insolvency law combines formal legal procedures, creditor participation, debtor protection, and professional intermediation within a structured debt-resolution process.The analysis further addresses why the Irish system may appear particularly complex in cross-border contexts. This complexity arises from the coexistence of several procedural routes, including bankruptcy, Debt Relief Notices, Debt Settlement Arrangements, and Personal Insolvency Arrangements, each with distinct eligibility requirements, debt categories, creditor approval mechanisms, procedural safeguards, and consequences for discharge. Personal insolvency law in Ireland is governed by the Bankruptcy Acts 1988 to 2015 and the Personal Insolvency Acts 2012 to 2021; the Personal Insolvency Act introduced three central methods of debt resolution and amended bankruptcy law. The involvement of multiple institutional actors, including courts, the Insolvency Service of Ireland, creditors, approved intermediaries, and PIPs, further contributes to the system’s layered structure. This makes Ireland a useful example for examining how legal tradition, statutory reform, institutional design, and practical access to professional guidance interact in personal insolvency proceedings.Moving beyond descriptive and comparative approaches, the paper demonstrates that divergences in the treatment of tort-based claims give rise to significant challenges in terms of legal predictability, enforceability of claims, creditor protection, debtor rehabilitation, and the overall level of trust in cross-border insolvency systems. These challenges are particularly evident in the inconsistent delineation between dischargeable, non-dischargeable, excluded, and conditionally includable obligations, differing evidentiary and procedural requirements, and varying policy priorities regarding the balance between creditor protection and the debtor’s economic fresh start.Against this backdrop, the paper develops a structured conceptual framework designed to systematically address these issues. The framework identifies key dimensions of legal divergence, including the legal origin of the claim, the nature of the harmful conduct, the degree of fault or unlawfulness, the procedural status of the claim, the availability of discharge, the involvement of institutional or professional intermediaries, and the rationale for excluding or limiting certain obligations within insolvency proceedings. These dimensions are translated into an analytical model that enables the classification, comparison, and evaluation of national approaches.Furthermore, the paper outlines how a structured understanding of these dimensions can serve as a basis for improving transparency, enhancing comparability, and reducing systemic tensions in cross-border contexts. Rather than advocating for full legal harmonization, the proposed framework provides practical orientation and evaluative criteria that can support academic analysis, professional decision-making, and institutional development. In doing so, the paper contributes to the development of solution-oriented approaches for managing legal diversity within the European insolvency landscape and aims to strengthen the stability, legitimacy, and trustworthiness of cross-border insolvency systems.
Pascal Verbracken
Open Access
Article
Conference Proceedings
Teaching with Passion, Assessing with Reluctance in the Age of AI: Exploring Educators Experiences of Teaching, Marking, and Algorithmic Assistance
This study explores educators’ experiences of teaching and assessment in the age of artificial intelligence, focusing on the tension between passion for teaching and reluctance toward marking. Teaching is often experienced as intrinsically rewarding, while assessment is frequently associated with workload, stress, and emotional labour. With the increasing integration of AI tools—such as automated grading systems and generative feedback technologies—these dynamics are being reshaped in complex ways.Using a survey-based design, the study captures educators’ perceptions of teaching, marking, and AI use in their everyday practice. Data are analysed quantitatively using composite scales to examine relationships between teaching passion, assessment reluctance, AI engagement, and well-being outcomes.The findings highlight how AI can both alleviate and reconfigure assessment-related labour, reducing some burdens while introducing new professional, ethical, and cognitive demands. The study offers insights into sustainable teaching practices and informs institutional strategies to better support educators in an evolving, AI-mediated educational landscape
Hope Iyobosa Izevbigie, Themba Ndlovu
Open Access
Article
Conference Proceedings
The Changing Landscape of Occupational Health and Safety in Mining: Challenges Posed by Emerging Technologies
The mining industry is experiencing a profound transformation, moving from Automated Mines (Mining 3.0) to Intelligent Mining (Mining 4.0) and the emerging Cyber-Physical-Social Systems of Mining 5.0. While these technologies promise higher productivity, they fundamentally reshape Occupational Health and Safety (OHS) paradigms. This study examines emerging risks, both new hazards and familiar risks appearing in novel contexts, associated with complex human–technology interactions.A dual-phase approach was used. First, a systematic literature review following the PRISMA Statement synthesized 32 high-impact studies from Scopus, IEEEXplore, and PubMed to explore OHS implications and global technological trends, including Internet of Things (IoT), Artificial Intelligence (AI), and autonomous vehicles. Second, an empirical study employed a targeted questionnaire for managers and executives at digitally transforming mining sites, assessing smart technology adoption (e.g., collaborative robotics, big data) and perceived impacts on risk management.Findings reveal that while digital strategies are established, critical gaps remain in addressing “soft” OHS factors such as mental health and organizational risks. Traditional physical hazards are mitigated through teleoperation and Proximity Warning Systems, yet new risks are emerging, including cognitive overload, stress from constant monitoring, and cybersecurity vulnerabilities. These results emphasize the need for human-centered OHS frameworks that address psychosocial and cognitive demands in Mining 4.0 and 5.0. By linking technological trends with practical managerial insights, this study offers guidance for achieving a safer, sustainable digital transformation in the mining sector.
Seyedeh Arezoo Baghaei Naeini, Adel Badri, François Gauthier
Open Access
Article
Conference Proceedings
Strengthening Cybersecurity and Digital Payment Literacy for Swiss SMEs Expanding into South Africa: A Train-the-Trainer Approach
Small and medium-sized enterprises (SMEs) increasingly rely on secure digital infrastructures when entering international markets. However, understanding country-specific cybersecurity regulations and digital payment systems remains a significant challenge, and existing Swiss internationalisation programs provide limited training in these areas. To address this gap, this study proposes a conceptual Train-the-Trainer model, which has not yet been empirically validated, designed to equip Swiss business trainers with expertise in cybersecurity and digital payment systems in Switzerland and South Africa. The proposed program consists of a seven-week training framework aimed at developing a pool of trainers capable of transferring this specialized knowledge to Swiss SMEs. By strengthening trainers’ competencies, the model supports SMEs in navigating regulatory and technological environments during market entry. The initiative is expected to generate direct benefits for business trainers and SMEs while also contributing to broader local, national, and regional capacity-building outcomes.
Franka Ebob Ebai, Petra Maria Asprion, Mosupye-Semenya Lebogang, Ntswaki Matlala, Sergi Gubin
Open Access
Article
Conference Proceedings
Software Features in eHealth and Speech Applications for Long-Term User Adherence: A Bibliometric Analysis
eHealth applications have become increasingly important in supporting healthcare delivery, rehabilitation, and behaviour change, with speech-language therapy emerging as one of the domains benefiting from these technologies. However, the specific software features and design trends driving engagement and effectiveness in this field remain insufficiently mapped. This research applies bibliometric analysis to identify emerging trends in software features implemented in eHealth and speech therapy applications. The analysis included 560 publications retrieved from IEEE Xplore, MEDLINE, Scopus, and the Web of Science Core Collection databases. Performance analysis examined publication trends, geographical distribution, publication sources, and citation impact, while science mapping was conducted to identify key research themes through keyword co-occurrence analysis. The findings reveal that eHealth research is strongly interconnected around themes including gamification, mobile applications, health games, motivation, physical activity, and medication adherence. Gamification represents a central research theme, emphasizing the role of game elements, motivational mechanisms, and Self-Determination Theory in promoting user engagement and long-term participation. Mobile applications provide the main technological context for implementing these mechanisms, while serious games demonstrate their application in healthcare interventions. The results highlight that effective eHealth solutions, including rehabilitation and speech therapy, require not only technological innovation but also theoretically grounded approaches integrating motivation and behaviour change principles.
Dijana Plantak Vukovac, Tatjana Novosel-Herceg, Ana Karlica
Open Access
Article
Conference Proceedings
Governing Agentic AI in Enterprise Operations: Architectural “Rails” for Safe, Deterministic, and Compliant Autonomous Systems
As enterprises accelerate the adoption of autonomous and agentic AI, the need for robust governance has become a critical architectural priority. Large organizations operate under strict regulatory, operational, and financial constraints, where even a single incorrect payment, billing error, or missed reconciliation can lead to significant compliance violations, audit failures, and material financial losses. These environments depend on deterministic, traceable, and verifiable execution; therefore, AI-driven automation cannot operate freely but must be deployed on well‑defined “rails” that enforce consistency, accountability, and operational safety. This paper argues that the introduction of agentic AI requires a substantial expansion of traditional enterprise architecture principles to address new behavioral, security, and governance risks emerging from non-deterministic AI systems interacting with heterogeneous operational platforms-ERP, HCM, CLM, asset management, workflow engines, and domain-specific applications. We propose a governance-centered framework for safe agentic AI in enterprise settings, emphasizing lifecycle oversight (model management, testing, deployment, rollback), cross-system policy enforcement, and auditable decision lineage. Central to this framework is a security model grounded in Just‑In‑Time (JIT) and Just‑Enough‑Access (JEA) permissions, ensuring that AI agents receive only the minimal privileges required, only when needed, and never with long‑standing or system‑wide access. Additional safeguards include least‑privilege design, segmentation boundaries, continuous audit trails, agent identity isolation, controlled inter-agent communication, and human‑in‑the‑loop escalation for high-risk or sensitive tasks. These controls prevent unauthorized lateral movement, protect sensitive financial and HR data, and ensure agent actions remain aligned with organizational risk and compliance boundaries. By integrating these governance mechanisms with orchestration and policy engines, enterprises can achieve predictable execution, transparent reasoning, and resilient automation at scale. This work highlights why governance is not peripheral but foundational to the safe deployment of agentic AI
Elizabeth Koumpan, Vimal Dimpi
Open Access
Article
Conference Proceedings
Driver Interaction, Needs, and Preferences for Steering-Wheel HMIs: An International Survey
The increasing integration of Advanced Driver Assistance Systems (ADAS), infotainment platforms, and connected technologies has transformed the steering wheel into a central Human–Machine Interface (HMI) for drivers. Despite this evolution and the growing complexity of automotive HMIs, quantitative studies providing a holistic understanding of drivers’ interaction patterns, preferences, and unmet needs regarding steering-wheel interfaces remain limited. This study aims to characterize current driver interaction with steering-wheel HMI technologies and identify design implications for future automotive interfaces through an international online survey conducted among drivers holding a valid motor vehicle driving license. A total of 280 participants from multiple nationalities completed the questionnaire, covering driver profiles, vehicle technologies, steering-wheel interaction and usability. Data were analysed using descriptive statistical methods to identify interaction patterns, usability challenges, and user expectations.The results indicate that steering-wheel controls are particularly relevant for infotainment and communication functionalities, with nearly 80% participants reporting regular use. In contrast, ADAS-related steering-wheel controls presented lower usage levels. Although participants generally perceived steering-wheel layouts and icons as intuitive and recognizable, difficulties in locating functions, non-intuitive layouts, and excessive numbers of physical buttons were identified as relevant usability challenges. Participants showed a preference for customizable controls, simplified layouts, and adaptive functionalities to improve convenience while reducing interaction complexity and driver distraction.The findings provide empirical insights into current driver interaction with steering-wheel HMI technologies and support the user-centred design of future automotive interfaces. The design implications can assist researchers and manufacturers in developing safer, more intuitive, and adaptable steering-wheel HMIs.
Micael Gonçalves, Vladimiro Lourenço, Lídia Lemos Ribeiro, Ricardo Silva, Nélson Costa
Open Access
Article
Conference Proceedings
Comparative Analysis of Steering Wheel Button Types, Layouts and Functions in the Market
Modern steering wheels have evolved from simple mechanical steering devices into complex human-machine interfaces (HMIs), incorporating controls for multimedia and communication, navigation, driver assistance systems and comfort features. This proliferation of functions has increased both the number and diversity of physical controls available to drivers. Understanding how different manufacturers currently address this design challenge, namely which button types they favour, how they group different functions, and where they place controls on the steering wheel, constitutes an important step prior to proposing new interface concepts.This paper presents a comparative market analysis of steering wheel button layouts and functions across six benchmark automotive brands. A total of 69 physical controls were analysed and classified according to input type (press, touch, roller, and hybrid combinations), functional category (Multimedia and Communication Control, Navigation and Menu Control, Cruise Control, and Customization and Others), and their spatial location on the steering wheel. The objectives of this study were to (i) identify the button types most commonly used in current production steering wheels and examine their association with the functions they perform, and (ii) analyse the spatial distribution of these buttons on steering wheels to identify current design practices.The brand-by-brand survey was complemented by an infographic-based visual analysis that mapped button types and functional categories onto six anonymized steering wheel layouts. In addition, a survey was produced for each brand, documenting every control identifier together with its associated function, button type, and location (left side, right side, or central spoke).The results reveal a clear predominance of press-type buttons (59% of the sample). Disaggregating button-type distribution by functional category confirms this pattern, showing that press-type buttons are the most frequently adopted solution across all three quantifiable functional categories, with the highest share in Multimedia and Communication Control (65.2%), followed by Navigation and Menu Control (52.9%) and Cruise Control (57.7%). Conversely, touch-type buttons achieve their highest relative representation in Cruise Control (34.6%).Cruise Control also exhibited the greatest consistency in the spatial placement of controls across the different brands, whereas Customization functions showed the lowest degree of standardization.These findings characterize current tendencies within the analysed benchmark sample and provide an empirical foundation for the development of new steering wheel Human–Machine Interface (HMI) concepts capable of balancing driver safety, usability, and innovation.
Lídia Lemos Ribeiro, Micael Gonçalves, Nélson Costa
Open Access
Article
Conference Proceedings