AHFE Open AccessHuman Factors and Systems Interaction
Editors: Isabel L. Nunes
Topics: Human Systems Interaction
ISBN: 979-8-950676-06-2
DOI: 10.54941/ahfe1007249
Table of Contents
Paper Vision Town: Designing an Interactive System to Experience Color-Vision Diversity and Color Universal Design
Understanding the diversity of human color vision is essential for creating inclusive environments and products. Although color universal design (CUD) principles have been widely discussed in design and human-factor research, opportunities for non-experts to experientially understand color-vision diversity remain limited. Many individuals with typical color vision find it difficult to imagine how visual information is perceived by individuals with color-vision deficiencies, which can hinder the effective application of CUD in real-world design practices. To address this issue, this study proposes an experiential learning system called Paper Vision Town, which enables participants to intuitively understand color-vision diversity and the importance of universal color design through hands-on exploration.Paper Vision Town is a paper-based educational system that represents a city environment. Two versions of a city were developed: one designed for individuals with typical color vision and another simulating the visual perception of individuals with deuteranopia, one of the most common types of color-vision deficiencies. The visual elements of the cities, such as signs, maps, buildings, and public information, were carefully designed to reflect differences in color perception, while maintaining identical layouts and functional structures. In addition, a set of theme cards was created to guide participants’ exploration. Each theme card presents a specific task or scenario such as finding a destination, identifying important information, or navigating a city.First, participants select a theme card and then explore both city versions according to a given task. By comparing their experiences in the two cities, participants can directly observe how differences in color vision affect visibility, information recognition, and usability. This comparative exploration allows participants to recognize the challenges faced by individuals with color-vision deficiencies and effectiveness of design strategies based on color universal design such as the use of luminance contrast, patterns, shapes, and redundant visual cues.The proposed system emphasizes experiential, rather than abstract, explanations. Through physical interaction and visual comparison, participants are encouraged to reflect on their own assumptions about visual perception and develop empathy toward users with different color-vision characteristics. Paper Vision Town can be used in educational settings, workshops, and design training programs to support learning about inclusive designs and human factors.This study contributes to the field of applied human factors and ergonomics by presenting a tangible and easily deployable method for communicating color-vision diversity. The results suggest that hands-on comparative experiences are effective in raising awareness of universal color design and promoting the practical application of inclusive design principles. The proposed approach highlights the importance of experiential learning tools in bridging the gap between theoretical knowledge and real-world design practice.
Yuki Aoki
Open Access
Article
Conference Proceedings
Lower limb exoskeletons for orthopedic surgeons: A user-centred specification of design requirements
Orthopedic surgeons are frequently exposed to awkward and sustained body postures and repetitive or forceful movements during surgical procedures. These demands may contribute to the development of work-related musculoskeletal disorders (WRMSD), particularly in the lower limbs. Such conditions may negatively affect the surgeon’s motor skills and quality of life. Consequently, there is a pressing need for solutions that mitigate physical strain without interfering with surgical performance or concentration.Lower limb exoskeletons have emerged as promising assistive solutions, designed to reduce muscular and joint loading through mechanical or torque-based support. While their effectiveness has been investigated in industrial settings, their application in surgical environments remains limited and insufficiently tailored to surgeon’s needs.This study identifies and prioritizes user-centered design requirements for a lower limb exoskeleton intended for orthopedic surgeons operating in standing postures. A mixed-methods approach was employed, integrating a literature review, consultation with an exoskeleton distributor, and direct input from orthopedic surgeons at a Portuguese hospital. Requirements were prioritized using the Mudge diagram and classified using the Kano model, with Satisfaction and Dissatisfaction Coefficients calculated to determine their relative impact. Internal consistency was verified through Cronbach’s alpha.Results indicate that stability, freedom of movement, and long-term comfort are the most critical design priorities. The findings provide a structured, human-centered framework for the development of a lower limb exoskeleton, contributing to lower limb WRMSD prevention while ensuring clinical acceptability.
Catarina Santos, Ana Teresa Videira Gabriel, Cláudia Quaresma, Isabel L. Nunes
Open Access
Article
Conference Proceedings
A Human-Centered AI Task Management System for Cognitive Load Reduction and Decision Support in Industrial Plant Management
Industrial plant management environments are becoming more complicated, with managers having to integrate production operations, maintenance activities, safety compliance, manpower allocation, and operational documentation across many digital platforms. The extensive use of fragmented tools including spreadsheets, emails, dashboards, and calendars frequently results in information overload, an increased cognitive effort, and reactive decision making. To solve these issues, this article introduces AI TaskManager, a human-centered, AI-assisted task and workflow management system intended to aid plant managers in manufacturing and industrial settings. AI TaskManager was built with Google AI Studio, which allows for the rapid creation of a high-fidelity prototype without the need for traditional software development. The system supports important management workflows with natural-language interface and multimodal AI capabilities, such as AI-assisted task creation, automated budget estimation from structured data, technical drawing interpretation, and adaptive job prioritizing. These functions are combined into a single interface to improve situational awareness, minimize cognitive load, and maintain human decision authority. A usability and human performance evaluation was conducted with ten participants, including plant supervisors, engineering professionals, and graduate researchers with experience in operational task management. With an average job execution time of 2.4 minutes, a 93% task completion rate, and a 6% mistake rate, the findings show excellent system performance. The findings demonstrate that AI TaskManager effectively facilitates cognitive ergonomics, decision support, and human–AI collaboration in industrial plant management, underscoring the promise of human-centered AI systems to improve managerial performance and operational resilience.
Md Asfaqur Rahman, Md Masum Billah, Md Shahadat Hossain, Md Ahnaf Shahriar Tanim, Mohammed Munif Hasan, Wenhao Yang, Yueqing Li
Open Access
Article
Conference Proceedings
From Task to Intentionality Automation: Mitigating the Open-Loop and Metacognitive Gaps in Agentic AI Systems
Artificial Intelligence (AI) enables powerful capabilities that are transforming almost all sectors. However, the economic growth driven by AI comes at a cost, and its sociotechnical impacts are fraught with contradictions and paradoxes. As a result, several legal initiatives and risk management frameworks have been introduced to mitigate the various risks associated with AI systems. Agentic AI systems require even closer attention than traditional AI. While traditional AI has a narrow focus and responds to direct commands, Agentic AI emerges from combining multiple types of AI capable of planning, tool use, and multi-step execution. These systems can behave and interact autonomously, making decisions and performing tasks to achieve system objectives with minimal human oversight. Recognizing that Agentic AI represents a paradigm shift, this paper addresses its challenges from a Human-AI Interaction perspective. It examines the root causes and impacts of risks arising from the transition from Task Automation to Intentionality Automation, where the user manages outcomes and constraints rather than individual task steps. Key issues include the Open-Loop Control Gap and the Metacognitive Gap, whose relationship is fundamental to understanding the collapse of human oversight, as they represent two sides of the same coin in the loss of control. By analysing scenarios such as cybersecurity and healthcare, this paper identifies dimensions of user demand and identifies Ecological Interface Design as an ergonomic approach to ensure that as AI gains agency, the human retains authority and situational awareness.
Mario Simões-Marques
Open Access
Article
Conference Proceedings
Enhancing Learning Efficiency and Ergonomic Well-Being: A Comparative Study of Handwritten, AI-Assisted, and Digital Structured Note-Taking
In an intense and demanding academic setting, note-taking is a core part of learning, where students must concentrate for long periods of time. While traditional handwritten notes are usually associated with deeper learning, newer digital and AI-assisted tools are increasingly used to reduce effort and improve efficiency. From a human factor perspective, it is a bit difficult to compare these different note-taking methods when both learning outcomes and workload are considered together. Most existing studies focus on either learning performance or how technology works, making it difficult to understand the trade-offs when we switch between different methods. This study looks at how handwritten, stylus-based digital, and AI-assisted note-taking methods affect learning retention and perceived cognitive workload using a within-subjects pilot study(N=11). Learning performance was measured using immediate and delayed retention quizzes, and workload was evaluated using the NASA Task Load Index (NASA-TLX). The results show no significant differences in immediate recall. Otter.Ai, an AI-assisted notetaking tool, has higher learning retention than the stylus-based condition for delayed recall (F2, 20 = 4.30, p = 0.028), while the handwritten method didn’t differ significantly from any other. Significant effects were also observed in physical demand (F2, 20 = 6.85, p = 0.005), effort (F2, 20 = 7.25, p = 0.004) and in frustration (F2, 20 = 4.44, p = 0.025). Together, these results show a clear trade-off between learning and workload. The study emphasizes the need to evaluate and ensure the correct usage of technology to help learn deeply, not just the efficiency.
Jannatul Hur, Anirban Biswas, Sheikh Fuzael Rahman, Vincent Nyamollo, Adar Chowdhury, Dipankar Nandy, Yueqing Li
Open Access
Article
Conference Proceedings
SIGNPAL: A Human–AI Interaction Framework for Real-Time Sign Language Translation
Advances in artificial intelligence and human–computer interaction have enabled significant progress in assistive communication technologies for deaf and hard-of-hearing individuals who rely on American Sign Language (ASL). However, a persistent communication gap remains between ASL users and non-signers in everyday interactions. This study preliminarily evaluates the usability and performance of SIGNPAL, a high-fidelity AI-driven ASL interpretation system designed to provide real-time gesture-to-text translation, text-to-speech output, and customizable accessibility features to support two-way communication. Three participants with basic, moderate, and advanced ASL familiarity completed four usability tasks: real-time sign-to-text translation, accessibility customization, gesture recording and playback, and text-to-speech reply to generation. Objective measures included task completion time, translation errors, and recognition accuracy, while subjective usability was assessed using a 10-item Likert questionnaire and the System Usability Scale (SUS). The results show that SIGNPAL achieved an overall translation accuracy of 83.33%, exceeding the predefined performance threshold, and a 100% task completion rate. Response times remained below two seconds, supporting real-time interaction. Likert-scale ratings indicated high user satisfaction (overall mean = 4.5/5), and the mean SUS score of 90.83 classified the system as having excellent usability. Qualitative feedback highlighted the clarity of the interface and the usefulness of the recording-and-playback feature, with minor recommendations for improving text visibility. These findings demonstrate that integrating human factors principles with AI-driven gesture recognition can produce effective and user-centered assistive communication systems, supporting inclusive real-time interaction between ASL users and non-signers.
Nadia Islam Tanha, Md Mehedi Hasan, Roksana Haque, Md Mahamudul Hasan, Jannatul Hur, Yueqing Li
Open Access
Article
Conference Proceedings
Effects of Instagram Feature Usage Patterns on Subjective Prospective Memory and Personality Traits
As social media has become central to information acquisition and social interaction, increasing attention has been paid to how platform design and usage behaviors influence cognitive processes, particularly prospective memory. While short-form video platforms characterized by rapid context switching have been shown to impair prospective memory, empirical evidence regarding Instagram—an image-centered platform with diverse interactive features—remains limited.This study investigated Taiwanese Instagram users aged 20–40 to examine the relationships among usage behaviors, personality traits, and subjective prospective memory difficulties. Data were collected in December 2025 using a demographic questionnaire, an Instagram Usage Behavior Scale, the Prospective and Retrospective Memory Questionnaire (PRMQ), and the Big Five Inventory–10 (BFI-10). Analyses included reliability analysis, exploratory factor analysis, cluster analysis, one-way ANOVA, and Pearson correlation analysis.Results indicated that Instagram usage behaviors could be categorized into three latent factors: Public Posting, Unidirectional Browsing, and Direct Messaging, forming three user groups: Responsive Maintenance Users, Socially Engaged Users, and Information-Interactive Users. No significant differences in subjective prospective memory difficulties were observed among the usage groups. However, significant group differences emerged in extraversion and openness, with more interactive users demonstrating higher openness to experience. Additionally, frequent use of the Explore page and short-form video features was associated with higher levels of subjective memory difficulties. Correlation analysis further revealed a significant negative relationship between conscientiousness and prospective memory difficulties.Overall, the findings highlight that while general usage behavior types may not directly differentiate prospective memory performance, specific platform features and individual personality traits play a critical role in shaping users’ subjective memory experiences.
Hsin Yu Tsou, Jo-Han Chang
Open Access
Article
Conference Proceedings
Measurement and Evaluation of the Interaction Forces of Specific Hammer Drilling Strategies to Determine Requirements for Dynamic Hand-Arm Models
In the development of hand-held technical systems, it is crucial to consider the relevant subsystems (user, technical system, environment). As there are confounders for each of the three subsystems, acquiring the necessary knowledge about their interaction for product development is difficult. Test benches can replace individual subsystems with functionally equivalent structures and are subject to the actual loads from the applications like power tools. To reduce human influence, mechanical hand-arm models are used. These models exhibit vibration characteristics that can be proven to be equivalent to those of the human hand-arm system via mechanical impedance (MI) measurement, provided they are exposed to identical vibration excitations. Current research considers the influence of grip force to be more significant than factors such as anthropometry or push force. However, the influence of push and grip forces, which vary over time, on mechanical impedance is not considered. The objective of this work was to investigate this influence. To investigate this influence, a study is conducted with ten test subjects who used a hammer drill. The grip and push forces were measured. The test subjects performed three drilling strategies from the skilled crafts sector: one hole, a vent hole and five holes in row. The data show a grip force for single drilling that increases constantly during drilling. The same effect was also found for vent drilling. An opposite trend for the grip force was measured for five holes drilled in a row. Here, the grip force decreases on average over the five holes. These results form the starting point for developing the 'IPEK Hand-Arm Model of the Saurbier Generation' (IPEK-HAMS), as they reveal the fundamental requirements for a dynamic hand-arm model. In particular, these include the recording of time-varying force curves, which is a prerequisite for deriving the vibration behavior of the human hand-arm system. Building on the state-of-the-art HAM, this model enables the reproducible evaluation of power tools during application-oriented operation by taking grip and push forces that change over time into account.
Simon Saurbier, Markus Pisarzowski, Andre Becker, Lukas Kleinhans, Lukas Bunk, Dieter Krause, Sven Matthiesen
Open Access
Article
Conference Proceedings
Gamified, Learner-Built Applications for Language Study: Effectiveness, Limitations, and Implications
This paper explores how foreign-language learners can enhance learning efficiency by designing and using personalized digital applications rather than relying solely on commercial language-learning tools. While platforms such as Duolingo dominate the market, their course offerings do not always meet specific learner needs, exemplified by the lack of Thai courses for Japanese speakers. To address such gaps, the study presents two case studies in which learners created their own applications tailored to their goals, proficiency levels, and learning preferences. The first case examines a Japanese learner of Thai who developed a simple Python-based app to master Thai consonant characters. By incorporating randomization and active recall, the app transformed rote memorization into an engaging task and significantly improved retention. The second case focuses on an advanced Japanese learner of English who built a JavaScript-based vocabulary app targeting high-difficulty lexical items often neglected by mainstream apps, resulting in measurable vocabulary growth. The discussion highlights that these apps were intentionally designed for single users—the developers themselves—allowing extreme personalization without concern for general usability. Although such specificity limits generalizability, recent generative AI tools lower technical barriers, enabling more learners to create similar tools. Importantly, both applications are open-source, allowing others to adapt them to their own needs. The study concludes that learner-built apps hold great promise for personalized language learning, while also posing challenges for scalability and broader applicability.
Jun Iio
Open Access
Article
Conference Proceedings
FRAM analysis of a berthing accident in a high-throughput Brazilian port - failing safely in a complex and non-linear workplace
This paper presents an accident reanalysis using the FRAM (Functional Resonance Analysis Method) methodology to understand a vessel grounding that occurred during a night-time approach for berthing at a large-scale deep-water, multi-terminal industrial port complex on the Brazilian coast, designed for high-throughput cargo handling and continuous operations, with segregated access channels, breakwater-protected waters, and intensive vessel traffic management. Although this accident resulted in no injuries, environmental impact, or intense structural damage, it exposed latent vulnerabilities in maneuvering, port operations and coordination routines. The official investigation relied on a linear accident analysis combining the 5 Whys and a Fault Tree Analysis (FTA), noticing discrete basic causes and recommendations focused solely on procedure revisions and training. While useful for identifying missing barriers, this linear approach can under-represent the coupled, adaptive, and time-compressed character of port entry and berthing operations in high-complexity and high-traffic cargo facilities. The FRAM reanalysis, though, revealed tight coupled interactions involving passage planning, bridge operations, port communication, coastline visual navigation and support resources, unveiling a hidden complexity blurred by linear methodologies. Indeed, moving beyond linear cause-effect methodologies, this FRAM reanalysis provided a more coherent understanding of how organizational, technological, environmental, and individual factors interact to shape maneuvering performance in complex workplaces of a VUCA and BANI world. Therefore, to properly recognize the real work conditions and constraints that took place in the accident of this study, the FRAM was applied to comprehend its complex nature, especially during nighttime operations.
Josue Franca, Mônica Neves, Jeniffer Paula, Maria Luiza Castro
Open Access
Article
Conference Proceedings
Enhancing Human–System Interaction in Order Picking through Ergonomics-Based Decision Support
Human-centered decision-making is becoming increasingly important in logistics systems where operational performance relies heavily on manual labor. In food distribution centers, for example, order picking is one of the most physically demanding activities. However, task allocation practices often neglect worker-related characteristics, which can lead to imbalances in manual handling demands, physical overload, and long-term fatigue. An industrial case study revealed significant variability among operators in daily load handling and item manipulation, underscoring the necessity of decision-support tools that consider ergonomic factors. This study proposes a human-centered framework for order-picking task allocation that incorporates measurable worker characteristics, such as handling exposure and manipulation counts, into planning processes. By moving beyond the assumption of homogeneous operators, the framework enables more transparent and balanced decision-making while maintaining productivity. From a human factors and systems interaction perspective, this work represents workers as key system components rather than interchangeable resources. Embedding ergonomics requirements in ex-ante planning enhances interpretability, trust, and the overall quality of operational decision-making. Grounded in a real-world application at a large food distribution center, the approach aims to promote sustainable interactions between planning systems, decision-makers, and frontline workers in physically demanding environments.
Mónica Gaboleiro, Isabel L. Nunes, Maria Isabel Gomes
Open Access
Article
Conference Proceedings
Work Demands and the Impacts of Current Work Practices
The work demands are defined as the fundamental human interactions expected from users when interacting with a product, service, or system in a workplace to perform the desired work. If the human interactions involved in meeting work demands are not managed, they may negatively impact users. In the current evolving society, characterized by technological advancements and changing fundamental needs, tracking human interaction is becoming increasingly complex. The traditional techniques developed to understand user performance are becoming outdated due to new work practices, technologies, digitalization, and customization. This study focuses on developing a concept that maps the user interactions needed to complete the required work with the product, service, or system, as well as the possible adverse reactions that may arise due to the demands of the work. This study involves developing the Work Demands and Impacts (WDI) concept by integrating existing work-demand concepts and relevant models from the literature. The WDI concept illustrates the potential demands and impacts a user may experience in any workplace. The proposed concept is tested on two commonly observed day-to-day tasks to assess its effectiveness in mapping potential demands and impacts in the workplace. The WDI concept can assist designers involved in workplace operations in better managing human interactions. This will provide a new perspective in developing a safe and effective workplace while tracking user performance.
Dubesh Sai Mangam, Urmi R Salve
Open Access
Article
Conference Proceedings
Backhoe and Loader Excavator Operators’ Workplace Health and Safety and Gaseous Pollutants
Operating heavy machinery is both physically and mentally very demanding job, which still carries significant risks, especially at excavators, even with the current regulations in place. Given that working conditions have a substantial impact on the health and safety of both employees and local inhabitants of mining industry sites, it seems imperative to enhance them. The first part of this research surveys 45 backhoe and 22 loader excavator operators’ attitudes regarding their satisfaction about health and safety at workplace and gaseous pollutants, while the second measures gaseous pollutants levels in machines operation and compares it to regulation documents. The findings of gaseous pollutants’ measurements demonstrate that, even with new machines, there are occurrences where specific working condition parameters are exceeded. Mann-Whitney tests confirmed statistically significant differences between backhoe and loader excavator working conditions regarding measured average O2, NO and CO2, average overrun of NOx and relation between maximum relative value measured of NOx and allowed value in regulation documents, because the backhoe excavators have higher values of the examined parameters. The findings also show greater satisfaction of loader excavator operators, as Mann-Whitney tests confirmed statistically significant differences regarding importance of satisfaction with working environmental conditions, the air quality in the working environment and operators’ knowledge to maintain or improve health and safety. It is evident that the workplace of loader excavator operator has lower human health and safety risk levels then backhoe excavator operator workplace. The proposal for future research is deeper analysis of causes of those differences.
Vesna Spasojevic Brkic, Ivan Mihajlovic, Nemanja Janev, Martina Perišić, Abdulghder Mohamed Alsharif
Open Access
Article
Conference Proceedings
The Mediating Classroom: How Digital Learning Ecosystems Shape Students’ Lived Experiences in Tertiary Online Education
This study was premised on the notion that the success of any educational program depends not only on institutional backing but on its capacity to resonate with, and be shaped by, the narratives of learners. This is because, the rapid shift to online learning has moved the digital learning ecosystem—the integrated matrix of platforms, tools, pedagogies, and institutional supports—from a passive backdrop to the active environment of tertiary education. This ecosystem does not merely deliver content; it fundamentally mediates, constrains, and transforms students lived educational experiences. This study centers explicitly on this critical interaction, with the question: How do digital learning ecosystems impact the lived experiences of tertiary education students? Grounded in the systemic-structural theory of activity, which frames learning as a contextualized work activity, this research employs a cross-sectional quantitative design. Data was collected via questionnaire from six hundred and sixty-eight graduate students at the University of Ghana Business School, measuring perceptions of their digital ecosystem and its effects on their daily academic reality. The data was analyzed using structural equation modeling (SEM). The findings showed significant relationships between digital learning ecosystem and the facets of the students lived experience. it is concluded that positive enhancement of the pedagogical learning environment will positively enhance the student-system interaction, thus enriching their lived experiences in the digital learning ecosystem. This understanding is crucial for intentionally designing ecosystems that acknowledge and support diverse student lives, moving beyond technical implementation to foster genuinely human-centric online education.
Mohammed Aminu Sanda
Open Access
Article
Conference Proceedings
Avoiding black box problems by assigning an active role to humans in the control of autonomous AI: A methodological approach
From a human factors’ perspective, the combination of humans and autonomous AI presents a number of challenges. A major problem is the black box nature of AI. Humans are faced with the impossible task of evaluating AI-generated suggestions that they can no longer understand and taking responsibility for them. An effect even appearing when AI provides explanations. Further challenges include difficulties in developing adequate situation awareness, de-skilling, de-motivation, or automation complacency. In our research, we assume that these negative effects on humans are exacerbated by the black box nature of autonomous AI in conjunction with the passive role assigned to humans in terms of supervisory control. To address these two problems while still leveraging the benefits of autonomous AI, we turn to the concept of interpretable primitives. A primitive is an autonomous AI agent with reduced scope, so that its purpose and functioning are easy for humans to understand. To avoid the black box problem, many primitives that are understandable to humans are used instead of a comprehensive but incomprehensible AI. The human’s role is to orchestrate the primitives by defining strategies, setting priorities, or directing their deployment. In this way, humans are assigned an active role that includes task characteristics that are considered prerequisites for human engagement and up-skilling. The paper presents operationalized criteria and a method for identifying primitives.
Nerissa Dettling, Samira Hamouche, Julia Usher, Manuel Renold, Toni Waefler
Open Access
Article
Conference Proceedings
Communicating Uncertainty in AI-Based Decision Support Systems: A Comparative Study of Numerical and Visual Representations
AI-based Decision Support Systems (AI-DSS) are increasingly recognized for their significance in professional environments. A key challenge in human-AI interactions is effectively communicating the uncertainty inherent in AI recommendations, as this can influence performance outcomes. Various methods exist for representing uncertainty, primarily through numerical data or visual cues. While users often favor numerical probabilities for their perceived precision, these figures can be difficult to interpret. Conversely, visual representations may enhance understanding but tend to be less accepted by users. The existing literature lacks clear conclusions regarding the impact of these communication designs on user performance and cognitive load. This research examines the effects of two forms of uncertainty communication—numerical (decimal numbers) and visual (traffic light system)—on user performance and cognitive load. An online experimental study was conducted with 104 participants assigned randomly to either condition within an AI-supported customer service context. Participants responded to support request emails using AI-ranked response modules while retaining decision-making authority. Each participant engaged with ten vignettes and completed questionnaires measuring task load afterward; performance was assessed based on correctly answered vignettes. Results indicated no significant differences in task load between groups. However, notable variations in performance emerged when systems made errors, influenced by the communication design used. These findings suggest that effective uncertainty communication strategies may vary based on context and audience, offering valuable insights for designing AI-DSS.
Antonia Markus, Esther Borowski, Ingrid Isenhardt
Open Access
Article
Conference Proceedings
Situated Lightness: A Local-First WebAR Framework for Democratizing Digital Heritage in Rural China
Digital interventions in rural heritage sites often face a "double bind": the high-fidelity technologies (e.g., Native Apps, VPS) required for immersive storytelling are incompatible with the infrastructural realities of rural environments. This mismatch leads to "digital exclusion" and unsustainable deployment. In this paper, we present a Research through Design (RtD) inquiry into the "Nanshe Ancient Village" project. Through an iterative in-situ prototyping process, we developed a "Local-First WebAR" architecture that leverages infrastructural constraints as design resources. We articulate the "Situated Lightness" Framework, which comprises three dimensions: 1) Technological Permissibility, utilizing mapless geospatial calculation and hysteresis state machines to ensure resilience in disconnected zones; 2) Interactional Friction, reframing browser autoplay restrictions as "rituals of consent" to foster volitional engagement; and 3) Socio-Technical Accessibility, adopting a zero-installation, static-hosting model to democratize access for resource-constrained communities. Our findings demonstrate that "Appropriate Technology"-rather than high-tech acceleration-offers a more ethical and scalable pathway for re-enchanting public spaces and combating digital alienation.
Dylan Leem
Open Access
Article
Conference Proceedings
Enhancing the Trust of SLPs Towards AI-based Speech Therapy Tools
Artificial intelligence (AI) presents transformative potential for speech-language pathologists (SLPs) from early diagnostics to predictive modeling for augmentative communication and autonomous therapy; however, clinical adoption remains constrained by significant trust deficit regarding technological precision and clinical efficacy. This research addresses the socio-technical divide by introducing a human-factors-centric framework for the systematic design and evaluation of AI-driven speech therapy tools. Synthesized from the Digital Health Scorecard, the proposed framework delineates four fundamental pillars of validation: technical performance, clinical efficacy, human-centered usability, and cost-benefit transparency. The paper identifies actionable technical strategies, such as the integration of Explainable AI (XAI) and the utilization of geographically and demographically diverse training datasets to enhance predictive reliability and mitigate bias. The framework emphasizes rigorous alignment with Evidence-Based Practice (EBP) to ensure that digital interventions remain grounded in peer-reviewed clinical standards. This framework also provides a rigorous methodology for developers to align AI innovation with SLPs' professional values, facilitating a more effective integration of technology into human-centered clinical environments.
Connie Chen, Li Liu, Vickie Yu
Open Access
Article
Conference Proceedings
Socio-economic constraints and the 'illusion of choice': impact of alcohol excise tax policy on public health and alcohol-dependent labourers
This research investigates the intersection of European Union (EU) excise tax policies and their unintended consequences on public health and occupational safety. While excise taxes are framed as fiscal or health tools, they create a systemic "choice architecture" that disproportionately affects low-income, alcohol-dependent individuals. This study addresses a critical gap in human factors: the point where economic constraints remove human agency, forcing a shift from "beverage preference" to "pure alcohol volume maximization." Using a multi-faceted econometric approach, the study identifies an "income threshold of forced choice" across various EU jurisdictions. By calculating the Alcohol Volume Value (AVV) – the ratio of pure ethanol obtainable per unit of currency – the methodology demonstrates how current tax group structures (Directive 92/83/EEC) create "tax loopholes." These gaps allow high alcohol content products, such as fortified wines or high-strength beers, to remain the cheapest path to meeting biological dependency needs. Preliminary results indicate that as incomes drop below the "Choice Loss Threshold," consumers maximize ethanol intake, leading to significant cognitive decline and increased workplace hazards. The qualitative assessment of work safety reports correlates these fiscal disparities with a higher frequency of safety protocol breaches and industrial accidents. The paper concludes by proposing a revised taxation model unified by pure ethanol content. This approach aims to prevent the economic steering of vulnerable populations toward high-potency products, thereby enhancing labour market stability and safeguarding the human factor in industrial environments.
Agris Raipalis, Dace Raipale, Biruta Sloka
Open Access
Article
Conference Proceedings