Training, Education, and Learning Sciences

AHFE International

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

Training, Education, and Learning Sciences

book-cover

Editors: Salman Nazir

Topics: Training, Education, and Learning Sciences

Publication Date: 2025

ISBN: 978-1-964867-69-4

DOI: 10.54941/ahfe1006004

Articles

Enhancing Pilot Training Performance: A Scoping Review of Artificial Intelligence Technologies

This scoping review explores the potential of Artificial Intelligence (AI) to improve pilot training, a critical component in maintaining aviation safety. Traditional training methods, while foundational, are limited by cost, access to real-world scenarios, and adaptability to individual learning needs (AI Labs, n.d.). AI technologies, such as machine learning, computer vision, and natural language processing, offer innovative tools to enhance the cognitive and decision-making capabilities of pilots, enabling personalized learning, real-time feedback, and adaptive simulation environments (Kabashkin et al., 2023; Eddins, 2024). A comprehensive review of academic databases was conducted using specific keywords related to AI and pilot performance. Studies included in this review focused on AI applications that directly impact pilot training efficacy. Data were extracted and qualitatively analyzed to assess the scope and quality of findings across selected literature.Key findings reveal a shift from instructor-led and simulator-based training to more dynamic, AI-enhanced platforms that simulate real-time stressors and decision-making contexts. Additionally, AI supports safety outcomes by powering predictive analytics to identify potential risks before they occur (Mylrea & Robinson, 2023). Despite these advances, challenges remain. Ethical issues such as algorithmic bias and data privacy need to be addressed. Technical limitations, including the demand for high-fidelity simulation and AI reliability, also hinder widespread adoption. Human factors, notably pilot trust in AI systems, significantly affect implementation success (Korentsides et al., 2024). This review concludes that while AI presents transformative opportunities for pilot training, its effective integration requires addressing existing limitations, updating regulatory frameworks, and exploring synergies with technologies like virtual and augmented reality. Future research should emphasize collaborative AI systems and ethical safeguards to ensure both technical effectiveness and pilot acceptance.

Abner Flores, Alexander Paselk, Teresa Irish

Conference Proceedings

Developing a Post-Flight Debriefing Tool: Insights from a UX Workshop and Tool Demonstration

After flight training, student pilots and flight instructors debrief by discussing the trainee’s performance and sharing observations. To improve this process and promote more objective debriefing, a web-based debriefing tool was developed to integrate and present in-flight data (e.g., vector data and position, etc.) and operator data (e.g., physiological and psychometric data, etc.). The tool allows users to replay critical flight segments for improved flight training analysis for, but not exclusively, maritime drone crews. Its development followed a human-centered design approach, with insights gathered through a user experience (UX) workshop and follow-up tool demonstration with experimental flight crews (both in the role of trainees and trainers) in unmanned aviation. Participants evaluated the tool’s user experience before and after the tool customization. While the debriefing tool is perceived as rather neutral, participants embraced the concept and gave valuable insights on effective debriefing and on improving the tool. In general, we can state that the flexible design allows easy customization for diverse user needs, offering a promising framework for effective and more objective debriefing across various domains in aviation.

Maria Hagl, Darlann Bertolone Lopez-serrano, Marcus Biella, Alexander Donkels, Andreas Volkert, Sven Lorenz, Kristian Fettig, Nicholas Sloane, Andreas Pick

Conference Proceedings

Learning and Engagement in Continuous Professional Development Through E-Learning Platforms

Although significant emphasis is placed on continuous learning, most research focuses on formal education or non-formal education that aims to develop general competencies. Meanwhile, there is a lack of research on the development of professional competencies. General competence development focuses on broad, transferable skills applicable across various contexts, while the development of professional competence is focused on developing specialized, role-related expertise and acquiring technical knowledge in a specific field. Moreover, professional competence development on e-platforms requires addressing specific needs to ensure engagement in continuous professional development. The use of a personalized learning approach is one of the key factors to ensure engagement in continuous professional development. Personalized learning emphasizes the tailoring of content to align with individual learners' goals, roles, and skill levels, incorporating different layouts, presentation styles, and learning methods that can be selected based on the learner's specific needs. The paper aims to investigate the linkage between the elements of personalized learning and engagement in continuous professional development through e-learning platforms. In doing this, the quantitative data were collected from questionnaires distributed among customs and international trade professionals using simple random sampling. The study revealed that among customs and international trade professionals, the dominant learning style is kinesthetic, which emphasizes the need for creating highly interactive, practical, and task-oriented activities. The findings also highlighted customs and international trade professionals' preferences for content elements, e-learning platform functionalities, information sources, and other elements of personalized learning that enhance their engagement in continuous professional development.

Ugnė Savanevičiūtė, Gabija Savaneviciute

Conference Proceedings

Structural Hybridization of Teaching-Learning Platforms and Its Influence in (Re)Orienting Working-Students' Mental Modes for Knowledge Acquisition

The purpose of this paper was to understand how Structural Hybridization of Teaching-Learning Platforms (re)orient the mental modes of working-students in the process of quality knowledge acquisition. Since 2020, the world has faced a historic challenge caused by the COVID-19 pandemic whose spread across the globe temporarily brought a halt to economic, social and learning activities that form part of peoples everyday lives. In the academic sector, the “teaching-learning” activity, which used to be conducted face-to-face, was shifted to online using virtual platforms. Despite this structural shift, the use of face-to-face learning is still considered necessary, thus prompting its integration with the virtual platforms to create hybrid teaching-learning platforms. In Ghana most universities have Graduate programmes designed for working-persons that are delivered weekdays’ evenings and/or weekends, using the hybrid system, which enables a “teaching-learning” roll-over between face-to-face and virtual platforms. Despite the usefulness of such hybrid platform, its influence in (re)orienting the mental modes of working-students towards quality knowledge acquisition remains unexplored. Building on the notion that an individual’s self-regulation system takes shape and gets transformed over lengthy periods of time, with its problems and potentials being understood only against its own history, the argument that an individual’s mode of mental mode may result in his/her (in)ability to accurately acquire knowledge is explored, as underlined by the following question; Does the structural hybridization of onsite and online teaching-learning models entail new mental demands and systemic expectations from working-students in their pedagogical process of quality Knowledge Acquisition? This study is methodologically guided by the SSAT premise that the discovery of goals is essential to true activity that can be transformed into contradictions which may influence a person’s metal mode as well as expand into a qualitatively new organizational activity structure and systemic activity contexts. It was also encapsulated in Bedny and Karwowski’s well-established knowledge that activities of individuals are realized by goal-directed actions, informed either by mental or motor conscious processes, and the notion that one cannot perform a complex motor task without significant mental effort and concentration. Thus, the relationship between the different components of the working-students’ knowledge acquisition (i.e. motor and cognitive) is considered critical in evaluating the complexity associated, not only with the cognitive and motor aspects of the teaching-learning activity, but also with its emotional-motivational components. The systemic analytical approach is used to evaluate the cognitive aspect of complexity entailed in the working-students’ knowledge acquisition activity that depend on the specificity of information processing, and those emotional-motivational aspects of complexity that reflect the energetic aspects of their knowledge acquisition activity using the hybrid teaching-learning platform. This study is the first to be carried out in the education sector in Ghana and the findings will provide useful insight in the systemic design and structuring of hybridized teaching-learning systems (entailing a combination of face-to-face and virtual platforms) to enable quality transitions in the mental modes and wellness of working-students towards knowledge acquisition.

Mohammed Aminu Sanda

Conference Proceedings

Assessment of Georgian marine pilots training and certification system

Pilots with knowledge of local specifications have been employed to safely navigate the vessels for centuries already. Georgian pilots have been safely navigating ships without any major incidents so far. However, the maritime industry in Georgia is expanding rapidly, with new ports and larger vessels, which will increase the demand for highly skilled pilots. In order to maintain navigation safety and prevent accidents, it is essential to elevate the standards of maritime pilot services and identify the areas for improvement in national requirements. This study compares Georgia's marine pilot training and certification procedures with international guidelines and best practices of other countries. A qualitative analysis was conducted of the national legislation and regulations of Georgia and other marine countries that were thought to have the best practices in maritime pilotage. Also, compliance with IMO resolution A.960 (23) “Recommendations on training and certification and operational procedures for maritime pilots other than deep-sea pilot” was analyzed. Finally, a thorough review and comparison was conducted on the documents. Comparative analysis revealed the necessity to develop an enhanced and detailed supplementary regulatory framework pilotage certification program, having regard to the technical and natural characteristics of the region, as well as the best practices of other nations and recommendations of important advisory regulations.

Givi Tsitskishvili, Natia Dolidre, Avtandil Tsitskishvili

Conference Proceedings

A Generalized Combat Lifecycle Framework for Marksmanship Training and Assessment of Warfighter Readiness

Revelations in neuroscience and human performance optimization acknowledge the strong interplay of cognitive, physical and emotional functions as part of overall performance in both sports and in combat. Within the dynamic environment that is combat, the modern warfighter is required to apply fundamental and technical skills that span multiple levels of physical exertion and cognition concurrently. Furthermore, the modern warfighter must also effectively self-regulate emotional responses inherent to threat of physical harm while effectively task transitioning across these levels. As such, training for and assessments of combat readiness cannot consider individual attributes in isolation, but must pursue a comprehensive approach that is relevant to the requirements of the combat environment. The framework consists of multiple phases representing the progression of a combat event, from patrol/infiltration to initial contact to prosecution to reconsolidation. The phases are comprised of logical memory tests, aerobic tests, sustained power tests, and simulated assessments measuring both tactical acumen, threat identification, threat prioritization, predictive modeling, and other cognitive attributes critical to performance in combat. These tests are chronologically arrayed in a manner representative of the task transitions required by the majority of combat events. Utilizing this realistic framework for training and the resultant data collection enables leaders to acquire a comprehensive model of combat readiness using pillars of performance in an integrated fashion. This framework additionally provides a laboratory for leaders to test the impact of new training curriculum, approaches, and equipment in a contextually relevant manner. Beyond training, given the concern with traumatic brain injury, PTSD, and heavy-metal exposure in a combat environment, this framework provides an opportunity to baseline and measure the impact on integrated physical and cognitive performance over a warfighter’s career.

Caleb Weintraub, Dan Shultz, Lauren Reinerman-jones

Conference Proceedings

Rediscovering Puzzles

Many pioneers of Artificial Intelligence used puzzles to explore problem-solving models, such as Herbert A. Simon and Allen Newell. Their favorite puzzles include the Tower of Hanoi, which has been adapted in many textbooks in Computer Science. This paper explores extending classic puzzles to a new level for hands-on training, education, and learning. From the human factors point of view, we redesigned the Tower of Hanoi with compactness, mobility, configurability, reliability, and explainability in mind. We explored 3D printing methods with different materials, colors, and 3D models with usability measurements. We predict that physical puzzles can be used by children, college students, and seniors.

Yang Cai, Talia Perez

Conference Proceedings

Towards co-design workshops based on data-driven context detection: A pilot reflection workshop in childcare

In designing to improve the quality of childcare, it is necessary to empathize with stakeholders in daycare. In the childcare field, there is many context-dependent know-how that only the people in the field can understand. It is essential to involve designers and non-designers in co-designing. The recent development of information and communication technologies (ICT) has allowed the acquisition of in-situ behavioral data, which is defined as “a collection of specific information, referring to data from sensors, self-logging, telemetry, or social networks which capture people’s behaviors and patterns”. Although behavioral data has not been widely used in design, co-designers' behavioral data has the potential to play an important role in supporting co-design by providing a common ground. Developing tools to support reflection uses the field’s data. The materials provide a common understanding of the scene to be considered for reflection. However, selecting which scenes to reflect and post-data collection processing requires huge efforts. Thus, there is a need to automate the scene selection based on data-driven detection of the childcare contexts. As a step towards data-driven co-design workshops, this study aims to clarify the influence of scene extraction methods on reflection by childcare workers. We collected video recordings in a room mainly used for caring for one-year-old children at a nursery in Japan. The videos used for the workshops were recorded in two ways: upon staff’s individual request, namely self-logging, during caring, and automatic scene detection based on the sound levels. One staff member participated in the data collection. As a result, 16 videos were collected, and then the staff selected the two best videos for the workshop. Two videos were excerpted from a video having been captured whole day depending on the sound level. The excerpted videos had the largest sound level during the day.The workshop was conducted with eight childcare workers. The purpose of the session was to discuss daily childcare and how to take care of children and gain new insights. The participants reflected on each video of self-logged daycare scenes, and then on the auto excerpted videos. The reflection includes, for example, what happened, how the staff behaved, how the staff should have acted, and why the staff could or could not act ideally in the videos. The recorded videos were categorized, and the participants’ perceptions were transcribed for analysis. The videos of daycare were categorized into 9 categories based on video content, and brief notes attached to a staff recording request. The quotes related to video-based reflection and possible data for supporting reflection are extracted from the transcript of the workshop. Overall, the results imply that behavioral data and auto-excerpted recorded videos have huge potential to be used for reflection. The next step of this research is to clarify the issue and benefit of using behavioral data for co-design by conducting a workshop among designers and daycare staff to improve the quality of childcare.

Yuki Taoka, Sawako Fujita, Shigeru Owada, Shiori Fujimaki, Kaho Kagohashi, Momoko Nakatani, Shigeki Saito

Conference Proceedings

Understanding student experiences in remote learning setup: Qualitative analysis of causes and coping mechanisms for workload, stress and fatigue

This paper explores the factors contributing to students’ perceived workload, stress and fatigue in the remote learning setup and their coping mechanisms. A semester-long study was conducted during the remote learning setup in the University of the Philippines Diliman where 66 third year Industrial Engineering students participated. A total of 17 weekly online surveys were administered to measure students’ perceived workload, stress and fatigue, as well as open-ended items asking what contributed to their experiences and how they coped. This paper reports on the qualitative data collected in the longitudinal study. Simple descriptive analytics methods were used to analyze the qualitative data. Initial results showed that the top most frequent factors that affect workload and stress revolve around the given academic requirements as well as external events such as the national elections. As for fatigue, factors that were cited by students was lack of sleep. There were also observed trends on the responses – such that during the first few weeks, students were more concerned with student organizational work and completing their internships. Afterwards, these factors tend to decrease. Towards the end of the study period, the academic requirements, exams, and finals week factors were observed to be increasing.

Raymond Freth Lagria, Lorelie Grepo

Conference Proceedings

Competency Modeling in a Digital Age: Redefining skills and capabilities for a technologically evolving workforce

The rapid advancement of artificial intelligence (AI) and emergent technologies has revolutionized how tasks are performed across various domains (Dwivedi et al., 2021), in turn requiring a shift in the traditional competency model. These models now require more frequent updates to reflect the dynamic nature of technological evolution. In some contexts, AI surpasses human capabilities entirely (Zhang et al., 2020), consequently reshaping the landscape of required skills and knowledges for the tasks within the job, and requiring a deeper focus on improved decision-making. This transformation introduces a dual challenge: identifying and emphasizing new competencies to support decision-making while simultaneously reassessing tasks that are either obsolete or augmented by AI systems. For example, in areas like aviation, emergent aircraft designs have created a shift in which tasks that were previously reliant on humans (Vempati et al., 2021) such as controlling 8-rotors on an electric vertical takeoff and landing (eVTOL) aircraft, are now feasible only through AI systems. These agents often achieve optimal performance levels unattainable by humans, rendering traditional training for these tasks unnecessary. Conversely, in fields where AI complements rather than replaces human capabilities, such as cyber and intelligence, new responsibilities and knowledge requirements are being appended to existing roles. These additional knowledge, skills, and/or task requirements can lead to increased cognitive and operational workloads for trainees (Strauch, 2017). This dichotomy highlights the importance of distinguishing between the roles where training could be minimized due to automation and those where training must be expanded to accommodate the new tasks due to these technologies. Competency models in this digital age need to adapt to consider tasks based on their relevance and the level of AI integration. Models should be updated to include more decision making and highlight collaboration with AI systems to address the balance between task automation and human involvement. These changes can help to ensure that training programs remain efficient and relevant. The current work explores these challenges and offers a framework for designing competency models that reflect the evolving technological landscape. Further we propose strategies for identifying and incorporating updated competencies, emphasize the need for continuous model refinement, and outline methods to balance training requirements with operational demands. Key considerations include integrating AI-awareness into competency frameworks, reducing redundant training efforts, and fostering skills that enhance human-AI collaboration.By addressing these evolving needs, competency models can better prepare individuals for the demands of the digital age while promoting efficiency and adaptability in training programs. The paper aims to provide actionable insights and key considerations for organizations and educators tasked with developing competency frameworks. Ultimately, this work seeks to bridge the gap between technological capabilities and human potential, empowering individuals to thrive in increasingly AI-driven environments.ReferencesDwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994.Strauch, B. (2017). Ironies of automation: Still unresolved after all these years. IEEE Transactions on Human-Machine Systems, 48(5), 419-433.Vempati, L., Geffard, M., & Anderegg, A. (2021, October). Assessing human-automation role challenges for urban air mobility (UAM) operations. In 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) (pp. 1-6). IEEE.Zhang, X. Y., Liu, C. L., & Suen, C. Y. (2020). Towards robust pattern recognition: A review. Proceedings of the IEEE, 108(6), 894-922.

Maria Chaparro Osman, Cherrise Ficke, Julia Brown

Conference Proceedings

From Gaps to Gains: Exploring How Professional Diversity Influences Situational Awareness in Collaborative Environments

This study explores the dynamics of group performance in a situational awareness (SA) context, examining how professional backgrounds and familiarity with key domains such as CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosives) and Maritime Search and Rescue (SAR) influence overall performance. The findings reveal that professionally diverse groups could be critical in enhancing overall performance by utilizing collective expertise and bridging knowledge gaps among members.It was observed that professionally diverse groups could demonstrate a levelling effect, where members with limited or no familiarity in specific domains benefit from the expertise of their peers. Thus, participants with "little familiarity" in CBRNE achieved higher average SA scores in collaborative groups compared to their baseline performance, likely due to shared knowledge and collective problem-solving. This suggests that group interactions can mitigate the impact of individual knowledge gaps, fostering an environment where participants complement each other’s strengths.Furthermore, it was observed that professionals with higher familiarity in Maritime SAR, such as those with "Good" or "Very good" familiarity, consistently scored higher across all SA levels. Similarly, participants with "No familiarity" in Maritime SAR showed improved total SA scores when placed in mixed-expertise groups, underscoring the importance of group dynamism in bridging expertise gaps.This study highlights the importance of group composition and collaboration in enhancing situational awareness for all members in a dynamic environment. By balancing diverse professional backgrounds, collaborative environments enable robust decision-making and skill development, offering valuable knowledge for training and operational strategies in high-stakes environments.

James Badu, Natalia Andreassen, Rune Elvegard, Salman Nazir

Conference Proceedings

Embracing the Immersive Frontier: Exploring the Benefits, Challenges, and Potential of Virtual Reality Training for EPA Pollution Prevention in Manufacturing Facilities

Virtual reality (VR) technology has emerged as a promising platform for enhancing educational experiences through immersive, interactive simulations. This paper discusses the benefits, challenges, and learning advantages associated with designing and implementing a VR application and training simulation game focused on teaching users how to identify and solve pollution prevention issues—such as, air leaks, water leaks, chemical spills, and other sustainability concerns—within industrial manufacturing facilities. This project was funded by the 2024 United States EPA Pollution Prevention Grant Program: Environmental Justice in Communities. By simulating realistic industrial environments, VR allows learners to practice identifying and rectifying potential hazards without incurring the costs or risks associated with real-life training. One of the principal benefits of such a VR-based application is the ability to replicate diverse scenarios that may be difficult or expensive to create in physical training contexts. For example, learners can repeatedly practice detecting air leaks or responding to chemical spills in highly detailed, customizable simulations, thereby gaining confidence and proficiency in problem-solving strategies.By leveraging the power of gamification, the EPA pollution prevention video game captures the attention and interest of players, including those in environmental justice communities who have limited access to traditional educational resources. The interactive nature of the game allows players to actively participate in the learning process, making it more memorable and impactful. Using this technology, immersive activities are utilized at a low cost to communities and engage diverse populations of learners with unique training opportunities. Furthermore, the game-like structure of the application has shown to promote learner engagement and motivation. Gamification elements, such as scoring systems, interactive challenges, and immediate feedback, are found to enhance user interest and retention of learning materials. This dynamic, interactive approach has the potential to foster a more profound understanding of best practices in environmental management, eventually leading to safer, more efficient industrial operations. However, challenges remain in the development and implementation of these types of VR training applications. Upfront costs associated with hardware acquisition and software development, as well as the need for ongoing maintenance and updates, can be prohibitive for some organizations. Additionally, there is a learning curve for trainers, who must become proficient in VR technology to facilitate training sessions effectively. Although development costs and technical challenges may impede widespread adoption, the potential for enhanced learner engagement, improved knowledge retention, and robust data collection underscores the promise of VR as an innovative tool for environmental management training.The advantages provided by VR-based pollution prevention training and education are manifold. VR systems can collect detailed data on user performance, including response times, accuracy rates, and decision-making processes. This information offers valuable insights for researchers studying environmental management, learning science, and human-computer interaction. By analyzing user data, researchers can identify areas where learners commonly struggle, leading to more targeted and effective instructional strategies. Additionally, such insights can inform iterative improvements to VR scenarios, ensuring that the application remains current with evolving industry standards and regulations.

Michael Oetken

Conference Proceedings

Crafting Recall: Impacts of Narrative on Semantic vs. Episodic Memory & Perceptions for an Aviation Procedure

This work examines considerations for integrating narratives into instructional design for individual and team training. Narrative, touted for its potential to enhance comprehension and retention, serves an important role in professional learning in safety-critical domains such as defense and aviation. However, not much is known about “how” and “why” narrative works. This work synthesizes concepts, theories, and findings on narrative to address gaps in the literature on its use within simulation training, focusing specifically on enhancing the memorability of instructional narrative. We present a systematic framework for crafting memorable narratives to support episodic facilitation through the grounding and framing of simulation training. We also examined the applications of this framework, through a pilot study supporting a program of research on the role and value of episodic memory (EM) within aviation training, Participants (n = 53) reviewed a text describing the steps of an exterior preflight inspection as a procedural checklist or an instructional narrative, then completed a battery of tests of their episodic recall and semantic knowledge. The narrative intervention had positive and significant effects on EM, including composite measures of tacit knowledge and EM formation, on individual features of episodic representation, and on the degree of integration of EM. As anticipated, the use of narrative failed to have any effect on semantic memory, and there were no effects on a set of affective or motivational factors as conventionally associated with narrative. The results of our study advance the concept of episodic facilitation for instructional design and provide preliminary validation of an approach to the measurement of EM for instructional events. This research may provide researchers and training practitioners the basis of a toolkit for applying and assessing the use of instructional narratives for simulations in safety-critical domains.

Nathan Sonnenfeld, Alexis Sanchez, Nelly Dragu, Sierra Outerbridge, Blake Nguyen, Stephen Fiore, Florian Jentsch

Conference Proceedings

Collaborative Risk Assessment in the Arctic: Lessons from the ATOMEX Tabletop Exercise and INCLUS Assessment Tool

This paper explores the ATOMEX discussion-based tabletop exercise (TTX) and the experiences of using the INCLUS tool to map and assess risk perception based on different expertise. The study investigates how interactive browser-based assessment tool contributes to a shared understanding of risk evaluation in the Arctic. The paper discusses the importance of incorporating expert knowledge in shaping maritime risk awareness and decision-making processes. It highlights the need for expanded risk evaluation methodologies that prioritize key risk information and utilize visual representations of expert judgments. The survey conducted by the INCLUS tool with Arctic maritime rescue professionals and radiological and nuclear authorities provides insights into risk and preparedness perceptions. The findings emphasize the necessity of predefined guidelines, better training, clear roles and responsibilities, and improved communication systems to mitigate risks effectively. The ATOMEX exercise also emphasizes the complexity of Arctic maritime nuclear preparedness and the critical role of interactive data visualization tools like INCLUS in fostering a shared understanding of risks and enhancing collaborative decision-making.

Rune Elvegard, Natalia Andreassen, Emmi Ikonen, Minna Markkanen, Matti Kropsu

Conference Proceedings

Mobile Vision for Citizen Science

Rapidly growing mobile phone sensing and computing capacities create wonderful opportunities for environment monitoring and data analysis. Mobile vision combines digital cameras and machine vision algorithms on phone platforms for particular visual data analysis. We explore the phone "microscopy" with real-time machine vision computing and dynamic visual updating functions. We developed a semantic description of the shape and dynamics of the toxic algae Karenia Brevis, collected by phone that both humans and machines can understand. We also explore the system architecture of the mobile vision modules with the performance measurements.

Yang Cai, Kaytee Pokrzywinski, Richard Stumpf, Shuheng Cao, Kevin Do

Conference Proceedings

Rethinking Assessment: The Body as a Compass for Understanding

This research investigates the potential of utilizing sitting posture as a novel indicator of engagement and focus in different activities. Recognizing that even subtle shifts in how we sit can reflect our cognitive and emotional states, this study employs a sensor fusion IoT platform embedded within a chair to capture detailed postural data. By employing both quantitative and qualitative analysis, this research aims to determine if specific sitting postures can reliably correlate to a user's level of focus and attention, and present an alternative approach to assess and understand engagement by providing a non-intrusive, real-time window into an individual's cognitive state.We conducted an experiment to observe participants over a moderate period, between one and two hours, while engaging in either focused studying or passive streaming content consumption. The sensor array continuously monitors and records nuanced changes in sitting posture, including leaning, slouching, shifting, and micro-movements. This high-resolution data is analyzed to identify patterns and variations that correlate with different levels of engagement. To analyze the vast amount of postural data collected, we employed machine learning techniques. This allowed us to classify different sitting postures and identify patterns associated with varying levels of engagement during both studying and streaming activities.Furthermore, the sensor fusion IoT platform employs the collected data to generate a comprehensive report for each participant's sitting period. This report provided a detailed visualization of posture changes over time, highlighting key moments of engagement and disengagement, and offering insights into individual patterns of behavior. These personalized reports have the potential to be valuable tools for self-reflection and behavioral modification, allowing individuals to gain a deeper understanding of their own focus and attention patterns.

Rafael De Pinho Andre, Almir Fonseca, Lucas Westfal, Matheus De Carvalho, Gustavo Dos Santos, Henrique Beltrão

Conference Proceedings

Implications and Impact of Adopting Recommendations from the DOT/FAA/AM-24/20 Report on the Use of Electronic Health Records (EHR) to Support Pilot Aeromedical Certification

This study investigates the implications of adopting the recommendations outlined in the DOT/FAA/AM-24/20 report, which advocates for the integration of Electronic Health Records (EHR) into the pilot aeromedical certification process (Watson, et al., 2024). Given the increasing demand for modernization within aviation and the heightened focus on safety, the implementation of EHR systems in medical certification processes presents both opportunities and challenges. Provenzano, et al. (2024) argues that the adoption of “electronic health records offers improved communication and information sharing while reducing medical staff errors” (Provenzano, et al., 2024). This paper assesses the broad potential impact of EHR adoption on areas such as pilot recruitment, training, health management, privacy concerns, and aviation workforce dynamics. Furthermore, it critically examines the ethical, technical, and logistical considerations inherent in the transition to EHR-based certification. EHRs are expected to increase efficiency in healthcare delivery, improve healthcare quality, and relieve increased financial pressure (Basil, et al., 2022). Despite the potential for associated benefits however, “EHRs are potentially vulnerable to security concerns that may affect the confidentiality and privacy of patients’ personal information” (Basil, et al., 2022). The primary aim of this research is to evaluate how the implementation of EHR systems can enhance the overall efficiency, accuracy, and consistency of pilot aeromedical certification. The paper discusses the potential benefits of EHR adoption, particularly in addressing the growing pilot shortage and improving the tracking of chronic health conditions and/or health risk factors, such as obesity, elevated psychological fatigue, cardiovascular disease or diabetes, that may affect flight safety. The legal and ethical dimensions of managing pilot health data, emphasizing the importance of adherence to privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), and the need for informed consent from pilots regarding the use and storage of their health information are also explored. One key issue is the resistance from stakeholders, including pilots, medical examiners, and aviation organizations, represents another significant challenge. Pilots may be apprehensive about the transparency and accessibility of their health records, fearing potential impacts on career opportunities or insurance premiums. The potential workforce implications of adopting EHR systems are also discussed, particularly the shift in responsibilities for medical examiners and the demand for IT professionals skilled in managing electronic health records. Long-term, the use of EHR systems could provide significant benefits for active pilots, facilitating continuous health monitoring, early detection of medical issues, and more effective management of pilot health over time. This could lead to improved overall aviation safety, with proactive health management reducing the risk of incidents related to undiagnosed medical conditions. Future research should focus on the long-term effects of EHR integration, particularly its impact on pilot health outcomes, workforce shortages, and regulatory processes. The development of guidelines, training programs, and regulatory frameworks aimed at optimizing the benefits of EHR adoption for aviation safety and workforce sustainability is recommended.Keywords: Electronic Health Records (EHR), Pilot Aeromedical Certification, Aviation Safety, Privacy and Security, Pilot Recruitment, Workforce Implications, Data Security

Abner Flores, Andrew Pagan

Conference Proceedings

Exploring Jamaican Students’ Readiness using the Grade One Cognitive Skills Assessment: A Psychometric Analysis

Children who are starting primary school (first grade) are described as being at a transitional phase characterized by a shift in their mental development. As part of the entry requirements to grade one, schools generally require students to participate in some sort of assessment. In the Jamaican context, entry to grade one requires students to participate in the Grade One Individual Profile (GOILP), however some students are not able to take the assessment because of the timing of the assessment. In such cases the GOILP cannot be considered a readiness assessment for those students. Having an alternative assessment - Grade One Cognitive Skills Assessment (GOCSA) could help teachers to determine students’ cognitive skills relevant to what should be learned at grade one. The purpose of this study was to examine the relationship, construct and concurrent validity between the GOILP and the GOCSA that was developed as an alternative for use with grade one students. A cross-sectional design was utilized that assessed 238 students who were selected based on the consecutive sampling of their parents. Exploratory factor analysis was conducted for the constructs within the GOILP and the GOCSA. The results showed one component solution loading (.72 - .92) accounting for 74% of the total variance, while the GOCSA indicated four components solution (loadings .56–.89) accounting for 25% of the explained variance. Concurrent validity was established by testing the relationship between students’ scores on both instruments which found to both assessments had a moderate positive relationship (r = .58, p = .01). The findings suggest that the GOCSA measure can be used as an equivalent assessment to the GOILP.

Sharline Cole, Tashane Haynes-Brown, Lamoine Samuels-Lee

Conference Proceedings

Exploring AI Integration in Engineering Education: A Framework for Product Representation and Behavioral Insights

Artificial intelligence (AI) is reshaping engineering education by enhancing creativity, collaboration, and problem-solving. This paper introduces a conceptual framework for integrating AI tools into undergraduate and graduate engineering courses, focusing on product representation schemes such as geometric modeling, technical drawing, and visual communication. The framework aims to bridge theoretical knowledge with practical applications, ensuring improved educational outcomes.The study provides a structured approach to evaluating the potential impact of AI on student learning, emphasizing outcomes like creativity, technical accuracy, and proficiency with advanced design tools. Although empirical data collection is not yet available, the framework is illustrated using ongoing scenarios and case studies to demonstrate its adaptability to diverse educational contexts. It also aligns with ethical standards like data integrity and equitable access.The framework lays the foundation for future empirical research, promoting innovative curriculum development and instructional design by leveraging AI tools such as virtual reality environments and AI-enhanced CAD systems.

Stefano Filippi

Conference Proceedings

Game-Based Learning for AI Education: Systematic Review

The rapid development of artificial intelligence (AI) globally has pushed educational systems to focus on AI literacy in K-12 curricula. However, teaching AI at this level presents challenges due to the abstract and complex nature of AI concepts, which can be difficult for younger students to understand. Game-based learning (GBL) offers an effective solution, providing interactive and immersive experiences that can boost student engagement and help them better understand these complex concepts. This paper explores how to integrate GBL into K-12 AI education, focusing on key design elements, game mechanics that improve engagement and learning outcomes, and the challenges of implementation. Based on a review of relevant literature from 2019 to 2024, the study proposes several design principles and practical recommendations. The findings highlight the importance of selecting suitable game types for different grade levels and learning goals, using game mechanics that encourage both competition and cooperation, and structuring learning in phases to improve engagement and learning results. At the same time, the shortage of resources and the integration of games and curriculum objectives are still the main obstacles to the implementation of GBL.

Biyao Li, Fang Liu

Conference Proceedings

The Architecture and Early Results of the IL-PRO AI-Driven Immersive and Adaptive Learning System for Industrial Robotics

While many traditional approaches to robotics training have been successful, the expense, space, and hazards associated with industrial robotics can be prohibitive and limit the scale at which students can be trained. Use of advanced digital technologies such as XR environments can provide economic and safe training alternatives. Previously introduced in this same forum, the Intelligent Learning Platform for Robotics Operations (IL-PRO) is now operational and in use in an undergraduate credentialing course at a major university. IL-PRO uses a multi-modal approach to automating instruction. It leverages students’ verbal responses and actions, a pre-trained large language model, and machine-learned models within an immersive (VR) environment for learning operations of robotic arms. At the core of the IL-PRO experience is the deployment of an automated learning system (ALS) designed to track student learning progress to personalize feedback and select i learning tasks. The ALS currently accounts for students’ levels of conceptual understanding and their motor skills relevant to operating the IL-Pro virtual robotic arm. This paper describes the learning content and system design of IL-PRO as currently implemented and presents sample student performance data from a recent pilot of the system.

Seth Corrigan, Shahin Vassigh, Bhavleen Kaur, Mark Finlayson, Tisa Islam Erana, Giancarlo Perez, Bhanu Vodinepally, Biayna Bogosian, Mohammadreza Akbari Lor, Shu-ching Chen

Conference Proceedings

Experiential and Sustainable Tourism: Teaching with Artificial Intelligence to Native Corn Producers in Tlaxiaco, Oaxaca

Native corn production in Tlaxiaco, Oaxaca, is an ancestral practice deeply rooted in the region’s cultural and gastronomic identity. However, producers face significant challenges, such as limited technological access and high illiteracy rates, which hinder their ability to diversify income through sustainable experiential tourism. This research proposes an Artificial Intelligence (AI)-based teaching model that facilitates the training of native corn producers, enabling them to transform their agricultural products into culinary tourism experiences. The program leverages AI to create contextualized educational manuals, adaptive learning strategies, and interactive content, allowing producers to acquire essential skills without requiring literacy or advanced digital proficiency.The main objective is to empower the community, strengthen regional economies, and preserve cultural heritage by integrating AI-driven educational tools with traditional knowledge. Specific objectives include: (1) designing accessible manuals for teaching traditional gastronomic processes, (2) applying AI tools to generate visual and auditory learning materials, (3) training producers in preparing and presenting traditional dishes for tourists, (4) developing strategies for experiential tourism and sustainable commercialization, and (5) evaluating the program’s impact based on community participation and entrepreneurial initiatives.The methodology consists of three phases, tailored to the community’s socio-cultural conditions:Diagnosis and Development of Educational Materials – Conducting interviews with producers to assess prior knowledge and challenges, designing printed manuals with illustrated content and AI-generated audio recordings, and producing multilingual instructional videos with AI-assisted narration.Gastronomic Training for Experiential Tourism – Organizing hands-on workshops where producers learn to prepare and present traditional dishes, such as masita (corn-based dishes with beef or lamb), tasajo, chorizo, and machucadas (corn tortillas with regional chilies). The training integrates multisensory learning techniques, including tastings, live demonstrations, and guided cooking experiences for tourists.Implementation of the Sustainable Tourism Model – Designing interactive tourism activities such as guided visits to native corn fields, immersive traditional cooking classes, and tastings highlighting the distinct characteristics of native corn products. AI-powered market analysis will be used to optimize pricing and promotional strategies, ensuring sustainable commercialization.AI is applied in three key areas:Educational Content Creation – AI-generated illustrated manuals, multilingual audio guides, and interactive instructional videos.Personalized Learning – AI-powered virtual assistants answering producers’ questions, interactive diagrams explaining food preparation, and adaptive learning strategies tailored to different knowledge levels.Market Optimization – AI-driven analysis of tourism trends, pricing recommendations, and product promotion strategies based on gastronomic tourism data.The expected outcomes include training at least 50 producers in the first year, developing accessible educational resources, launching gastronomic workshops as a tourism product, strengthening the regional economy through food commercialization, and preserving traditional culinary knowledge for future generations. The initiative also aims to establish a sustainable tourism route in Tlaxiaco, showcasing native corn production and traditional cooking practices.The social and sustainability impact of this project is substantial. It fosters social inclusion by making training accessible to producers with varying literacy levels, empowers rural communities by promoting entrepreneurship, encourages responsible and sustainable tourism, and supports agroecological conservation efforts to protect native corn varieties.This initiative represents a pioneering effort to bridge traditional knowledge with emerging AI-driven educational technologies, creating a replicable and scalable model for rural development. By integrating AI into the learning process and sustainable commercialization of traditional foods, the project offers an innovative pathway for empowering indigenous communities, preserving cultural heritage, and promoting sustainable tourism in Tlaxiaco and beyond.

Diana Rubi Oropeza-tosca, Omar Jimenez-marquez, Rodolfo Martinez-gutierrez, Gaudencio Lucas-bravo, Clara Ivette Rincon-molina

Conference Proceedings