ObjectiveTo examine how task anomalies and time pressure influence cognitive workload in procedural tasks and to identify whether workload effects are transient or sustained.BackgroundProcedural tasks often involve sequential actions for which disruptions, such as operational anomalies or time pressure, may exceed operator capacity. Understanding the temporal dynamics of workload under these conditions is essential for designing adaptive systems to effectively support human performance.MethodThirty participants performed three cooking tasks under nominal and off-nominal conditions (trials with induced operational errors and time pressure). Workload was continuously assessed using subjective ratings and physiological measures including skin conductance level (SCL), percent change in pupil size (PCPS), and blink rate (BR).ResultsSubjective workload was consistently greater in off-nominal conditions. Physiological measures varied in sensitivity to the task condition and type of task: BR reliably indicated elevated task demands, PCPS was sensitive only under multiple operational anomalies, and SCL showed limited responsiveness to all factors. Two distinct workload patterns were observed: isolated operational anomalies led to transient workload spikes, while multiple accumulating anomalies produced sustained high-workload states, reflecting a "workload history" effect.ConclusionCognitive workload responses depend on both the type and accumulation of task disruptions. BR provides a robust real-time marker, while subjective ratings capture perceived effort.ApplicationFindings inform the design of adaptive support systems capable of distinguishing between momentary operational challenges and cumulative strain, enabling tailored operator assistance in procedural and safety-critical domains.
ObjectiveThis study aims to investigate how information processing tasks can be performed by hand-based gestures in immersive environments and to identify usable gesture designs.BackgroundWith advances in display and interaction technologies, immersive environments have evolved to support productivity-oriented activities, in which information processing plays a critical role. However, performing such tasks in immersive environments remains challenging and calls for intuitive interaction techniques aligned with users' cognitive processes. Gestures offer a promising approach by allowing users to externalize and manipulate information through bodily actions.MethodsUsing 19 cognitive processes outlined in the revised Bloom's taxonomy as representative information processing tasks, a gesture elicitation study with 15 participants explored how these tasks could be mapped to hand-based gestures (Study I). The resulting candidate gestures were subsequently validated and evaluated with 20 participants using a combination of objective and subjective measures (Study II).ResultsStudy I produced an initial set of 32 candidate gestures, which were validated and evaluated in Study II. Four recurring mapping strategies were identified: linguistic-symbolic, spatial-manipulative, metaphoric, and social-conventional mappings. A consolidation model was then introduced to identify a recommended set of 20 gestures based on their performance across multi-dimensional measures.ConclusionsInformation processing tasks can be translated into usable hand-based gestures. Future gesture design should treat information as an interactive entity while carefully considering ergonomics, semantic expressiveness, and gesture similarity to improve usability.ApplicationsThe recommended gestures, mapping strategies, and design considerations can inform the gesture design for immersive systems that involve information processing tasks.
ObjectiveThis study examined how learning, workload and search behaviors were impacted by a chatbot during a self-regulated Web search task, as opposed to more classic search engines.BackgroundArtificial intelligence technologies, including chatbots, are becoming increasingly accessible. These tools have been demonstrated useful to support self-regulated learning, mostly in structured learning contexts. The reduction in workload they offer may, however, prevent key learning strategies from being deployed, especially for Web search.MethodSixty participants were asked to answer a set of essay questions and to rate their workload, effort deployed and literacy while either gathering information from the Internet (Web condition) or by chatting with a chatbot (LLM condition) with the possibility of verifying information on the Web. Several key search strategies and chatbot interaction measures were extracted. A surprise memory test was also presented to evaluate how they learned the content addressed in the essay questions.ResultsMeasures of effort, mental workload, search behaviors and literacy differed significantly across conditions. Performance on the memory test did not vary. Multiple relationships with memory performance, including key Web search and verification behaviors, were found.ConclusionChatbots may help reduce workload and short-term learning with a chatbot may be more influenced by the nature of the interaction with the tool, rather than the tool itself.ApplicationEffective uses of chatbots may require learners to verify the content generated by the chatbot and to show superior engagement. Engagement-promoting learning activities should be considered when using LLM-driven agents to support self-regulated, Web-based search.
ObjectiveTo examine how cognitive workload influences physiological responses, driving performance, and subjective workload in underground coal mine transport, with comparisons between novice and experienced drivers.BackgroundCognitive workload is a key determinant of safety and performance in high-risk driving environments. However, little is known about how driver experience shapes responses to workload demands in underground coal mine transport operations.MethodsSixty drivers (30 novices, 30 experienced) participated in four experimental conditions (baseline, warning stimulus, distraction, dual-task). Data from 54 participants were included in the final analysis after excluding unusable recordings. Physiological indicators (heart rate, heart rate variability, respiratory rate, and variability), driving performance metrics, and subjective workload (NASA-TLX) were analyzed. Linear mixed models were applied to examine relationships among workload, physiology, and performance.ResultsIncreased workload elevated physiological strain and subjective demand while impairing driving performance. Novices exhibited a linear decline in performance, whereas experienced drivers showed possible nonlinear workload-performance patterns.ConclusionDriver experience moderates the workload-performance relationship. Integrating physiological, performance, and subjective indicators provides a more comprehensive understanding of workload effects in underground driving contexts.ApplicationFindings can guide the development of adaptive driver support systems that dynamically respond to workload fluctuations, enhancing safety, reducing errors, and improving operational efficiency in high-risk mining transport environments.
ObjectiveWe develop a dynamic model to understand how trust in automation evolves within teams through team member interactions, assessing whether trust converges or diverges over time.BackgroundExisting research assumes that individual and team trust in automation converges over time, but these studies typically last only hours or days. This assumption may not hold for long-duration missions, like deep space exploration, where team dynamics and trust might evolve in a contingent rather than convergent manner. Contingent behavior evolves towards different endpoints based on small perturbations, whereas convergent behavior evolves to similar endpoints.MethodsWe developed a stochastic, discrete-event agent-based trust dynamics model that goes beyond existing models that only consider past interactions with automation. Our model incorporates team conversations, individual automation experience, and turn-taking interactions.ResultsConsistent with human subjects data, the model showed divergent trust behavior where team members' trust levels did not converge to similar values over time.ConclusionsDynamical models of trust in teams can show contingent behavior. Trust in automation within and across teams can diverge, indicating a new mechanism for trust dynamics. Trust calibration strategies should address potential divergence.ApplicationsDesigners should consider the divergence of trust within and between teams, especially for long-duration missions. Methods to calibrate trust in this situation may include structured debriefs or shared automation feedback displays.
ObjectiveThis study examined whether cognitive load produces selective effects on different trust updating pathways in AI-assisted decision making.BackgroundAlthough cognitive load affects trust in automation, its influence on the mechanisms of trial-by-trial trust updating remains unclear.MethodsA dual-task paradigm embedded in a mining exploration task manipulated cognitive load while capturing dynamic trust calibration. Guided by a dual-pathway framework, we operationalized process-based (analytical evaluation of AI recommendation correctness) and outcome-based (heuristic reliance on task outcomes) trust updating pathways. Trust dynamics and behavioral reliance were examined using linear mixed-effects models.ResultsCognitive load shifted the relative influence of the two trust updating pathways. Process-based updating was attenuated under high cognitive load, indicating reduced sensitivity to AI recommendation correctness during trust updating. Outcome-based information gained greater influence under high load, amplifying outcome-driven bias regardless of recommendation correctness. Asymmetric trust updating was evident overall, although the influence of cognitive load on this asymmetry depended on task outcomes. Overall, high cognitive load elevated both subjective trust and behavioral reliance on AI.ConclusionCognitive load shapes trust calibration through mechanism-level reconfiguration rather than global impairment. By revealing how cognitive constraints rebalance dual trust pathways-weakening analytic evaluation while amplifying heuristic outcome reliance-this study advances theoretical understanding of dynamic trust in human-AI collaboration.ApplicationThe results provide practical guidance for the design of AI systems in high-stakes settings, highlighting the need to support analytic trust updating and mitigate over-reliance under cognitive strain.
ObjectiveThis narrative review examines the cognitive, metacognitive, and team competency requirements that may contribute to productive and reliable collaboration between human and AI to address two questions: What capabilities make AI a competent collaborator? What makes humans ready for AI collaboration?BackgroundAs AI systems are increasingly integrated into workplaces and framed as teammates rather than tools, humans face challenges that include maintaining situation awareness, calibrating trust, and working with systems that may surpass them cognitively. We analyzed Human-Agent Teaming (HAT) readiness around two complementary levels: operational team competencies (communication, coordination, and adaptability) and regulatory capacities (trust calibration and metacognitive awareness).MethodWe conducted a structured narrative review of literature from 2010 through January 2026, searching Google Scholar, Scopus, PsycINFO, IEEE Xplore, ACM Digital Library, and Semantic Scholar, complemented by forward citation tracking. After screening 572 records, 192 articles were included for synthesis.ResultsCommunication inflexibility, limited shared understanding, and trust miscalibration emerge as recurring barriers to HAT, while regulatory capacities (trust calibration and metacognitive awareness) represent particularly critical dimensions of HAT readiness that remain to be fully operationalized.ConclusionHAT requires mutual readiness, with both humans and AI developing metacognitive and adaptive capabilities. Despite methodological heterogeneity limiting clear conclusions, cross-training and co-learning methods offer a promising avenue for building shared understanding and calibrated collaboration.ApplicationThis review provides practical principles for designing AI systems that support calibrated collaboration and for preparing humans to work adaptively with AI, thereby enhancing team effectiveness, reliability, and resilience in collaborative work environments.
Objective To develop and preliminarily validate a behavior- and height-based strategy-group-specific dynamic seat-adjustment model that generates coordinated low-frequency seat trajectories to reduce subjective discomfort during prolonged public-transport sitting. Background Dynamic seating may relieve sitting-related discomfort, but existing strategies rarely model coupled seat adjustments while considering behavioral and anthropometric variability. Methods Sixty-six adults completed counterbalanced 2-h resting and phone-use sessions. Every 15 min, they adjusted seven seat dimensions to their preferred configuration and rated overall and regional discomfort before and after adjustment. An improvement-driven weighted multi-output random forest was trained within Behavior × Height-Based Strategy Group strata, using elapsed sitting time as the only input and seven-dimensional seat configuration as the output; discomfort improvement was used only for sample weighting. Predicted configurations were smoothed into low-frequency trajectories. An independent within-subject crossover experiment compared Static, Mean-trajectory, and Dynamic conditions. Results Preferred configurations differed by behavior and height-based strategy group and changed systematically over time. Multi-output forests outperformed independent single-output models, supporting coordinated trajectory learning, whereas improvement-driven weighting mainly provided a relief-oriented training bias. In validation, the Dynamic condition reduced overall discomfort relative to Static seating across both behaviors and time points. Compared with the non-individualized Mean-trajectory baseline, Dynamic seating produced additional region- and behavior-dependent perceived-comfort benefits, especially in neck and trunk-related regions. Conclusion A stratified multi-output model can generate coordinated time-varying seat trajectories associated with lower subjective discomfort. Application This framework offers a preliminary basis for low-frequency passive adjustment in transportation seats with comparable geometry while requiring limited passenger input.
ObjectiveTo investigate how different types of automated cues (direct versus indirect) of a decision support system (DSS) affect performance and attentional mechanisms in visual search.BackgroundVisual search tasks in safety- and health-critical domains are increasingly supported by DSS. While such systems commonly provide explicit cues, these cues differ in whether they directly indicate target locations or merely signal target presence. Empirical evidence comparing these cue types, particularly at the level of attentional processes, remains limited.MethodParticipants completed a simulated medical visual search task in which they searched for target letters embedded in noise. Using a within-subjects design, participants performed the task under three conditions: no DSS support, indirect cueing, and direct cueing. Human-alone performance exceeded system-alone performance. Performance was assessed using signal detection measures, and response times and eyetracking were used to examine attentional guidance and search termination.ResultsDirect cueing increased sensitivity relative to indirect cueing and no support. Response times and eyetracking measures showed that direct cues guided attention more efficiently and reduced search effort, whereas indirect cues produced attentional patterns descriptively similar to the no-cue condition.ConclusionDirect cues enhanced sensitivity and attentional efficiency in visual search, whereas indirect cues provided little benefit over unaided search when human performance exceeded system performance.ApplicationThe results highlight cue format as a critical design factor for DSS in applied visual search, suggesting that location-specific cues are particularly effective when DSS complement rather than outperform human observers.
ObjectiveThis work analyzes the influence of egocentric versus exocentric viewpoint on the ability of a human supervisor to appropriately takeover or handoff manual control during a spacecraft's automated rendezvous and docking (ARD) motion.BackgroundPrevious work showed that automated spacecraft motion factors of initial condition and path curvature influenced supervisor perception of the system and subsequent decision to take over manual control. It was hypothesized that viewpoint may influence the human supervisor's interpretation of motion trajectories.MethodsA simulated automated docking procedure was monitored using a Virtual Reality headset by n = 42 participants from three viewpoints (one egocentric and two exocentric perspectives). Monitoring path motion with no prior knowledge of the spacecraft's targeted dock, participants determined which of two docks the spacecraft was targeting and either asserted manual control or relied on the automation to complete the maneuver. Decision outcomes were analyzed between viewpoints to assess if differences in perspective influence operational decision making on spacecraft paths.ResultsThe egocentric viewpoint supported correct takeover and handoff decisions further from the dock. Path characteristics that enabled better or worse decision-making performance differed between egocentric and one exocentric viewpoint but were similar to the other exocentric view.ConclusionIn addition to existing factors of initial condition and path curvature, supervisor viewpoint significantly influences ARD operational decision making. However, these factors did not account for all differences in outcomes between viewpoints.ApplicationFindings inform human-aware motion plan algorithm design for automated spacecraft systems. Locations of cameras used by observers of ARD maneuvers can also be informed.
ObjectiveWe examined how response-effect (RE), stimulus-response (SR), and stimulus-effect (SE) compatibility jointly influence performance in lever tool use, and test the robustness of previous results across different input modalities.BackgroundAccording to the ideomotor principle, motor actions are selected via anticipating their effects. This becomes particularly relevant in tool use, where the relationship between hand movement and tool movement might be inverted. While various compatibilities are known to influence performance, their interactions remain poorly understood.MethodWe built upon work by Müsseler and Skottke (2011) using orthogonal manipulations of RE, SR, and SE compatibility. Across four experiments with student samples (2022-2024), we varied input modality (button presses, continuous sliders, touchless gestures) and lever rotation type (discrete vs. continuous) to assess the generalizability of the original findings.ResultsMüsseler and Skottke (2011) showed that the interaction of SR and SE compatibility depends on the RE compatibility condition. Consistent with this, we found that under RE compatible conditions, performance was improved when SR and SE compatibilities aligned. However, under RE incompatible conditions, the SR × SE interaction disappeared with button press responses (Experiments 1 and 2) or reversed with more continuous responses (Experiments 3 and 4).ConclusionThese findings highlight the dynamic interplay of various compatibility relations for untrained participants, and that this interplay depends on, for example, the input device.ApplicationOur results inform the design of human-tool interfaces by highlighting when aligning response-effect mappings benefits performance and when mismatches can alter or even reverse other compatibility relationships.
Background Prior work on trust in multi-component systems has proposed two competing perspectives: system-wide trust (SWT) versus component-specific trust (CST). SWT argues that individuals view multiple agent teammates as interconnected parts of a single “system,” and thus trust in one agent spills over to others; CST, in contrast, argues that trust is evaluated on a component-by-component basis. However, existing studies have largely overlooked individual differences in these trust evaluation patterns. Method We conducted a lab study with 30 two-human-two-agent teams performing collaborative block-moving tasks. Teams completed 10 trials each under three agent reliability pairing conditions: perfect (both agents reliable), mixed (one reliable and one unreliable), and imperfect (both unreliable). After each trial, participants rated trust in each teammate and the team, while communication logs and task completion times were recorded. We first evaluated individual variability and classified participants based on how trust in one agent changed as a function of paired-agent reliability. Subsequently, we examined how these influenced communication behaviors and performance. Results We identified three distinct trust bias patterns: assimilation (trust becomes more similar across agents, consistent with SWT), no bias (trust remains independent across agents, consistent with CST), and contrast (trust becomes more differentiated between agents). These were associated with different communication strategies and reliance behaviors, which in turn affected team performance. Conclusion Trust in multi-agent HATs cannot be fully explained by SWT or CST alone but instead varies with how individuals comparatively evaluate autonomous agents. Application The findings provide a foundation for developing personalized trust bias mitigation.
ObjectiveSanding strategies were identified that correlated with better performance and less physical stress.BackgroundSanding requires manual skill because of the dexterity and perceptual needs to perform the job well, and it involves exposure to physical stresses.MethodsForce and tool kinematics were measured using an instrumented platform and motion capture for 20 participants while performing a timed paint removal sanding task in the laboratory. Performance was based on the quantity and uniformity of the paint removed and measured using image processing. The ACGIH Threshold Limit Value (TLV) for Hand Activity Level Peak Force Index was used to evaluate physical stress.ResultsLinear regression and machine learning algorithms revealed that high performers (above the 75th percentile) moved the sander more, while they varied but applied greater force compared to the low performers (below the 25th percentile). Some high performers could maintain a TLV below the acceptable limit by pausing more and applying less force but performing more frequent exertions.ConclusionsMore force, greater variations of force, and speed were related to better sanding performance, while less force and more frequent exertions and pauses helped reduce physical stress.ApplicationsStrategies identified in this study may be useful for training operators in sanding tasks for performance and safety.
ObjectiveWe examine how artificial intelligence (AI) roles (teammate, support, tool) shape acceptance via the mind perception subdimensions of agency and conscious experience.BackgroundAdoption of the term AI "teammate" has outpaced evidence of the term's impact. Such reframing does not guarantee that the benefits of human teamwork will extend to human-AI teams. Understanding the potential benefits and risks of reframing AI as a teammate is essential for guiding effective integration.MethodsAcross three studies-one survey of employees who use AI at least weekly (Study 1) and two experimental vignette designs (Studies 2-3)-we examined the relationship between AI roles and acceptance using both technology-centered (technology acceptance model, cognitive trust) and human-centered (affective trust) perspectives. This design distinguished role perception from presentation (Study 1 versus Studies 2-3).ResultsAs hypothesized, "teammate" was associated with higher AI mind perception relative to less collaborative roles. While perceiving AI as a teammate showed only positive relationships with acceptance, presenting AI as a teammate was negatively related to some acceptance outcomes after controlling for mind perception. Further, agency and conscious experience were differentially related to technology- and human-centered outcomes, supporting the importance of integrating both perspectives.ConclusionShifting AI roles from tool to teammate may enhance acceptance via mind perception, but forcing labels may have hidden costs.ApplicationsCollaborative AI roles such as "teammate" should be strategically implemented and aligned with intended mind perception and outcomes. Intermediate labels such as "support" may be more appropriate for describing the roles of today's AI systems.
ObjectiveThis review systematically investigates the determination of sufficient time budgets in conditionally automated driving, focusing on the interplay between takeover time, allocated time budgets, and outcomes.BackgroundConditionally automated driving requires human drivers to resume vehicle control within limited time budgets when system limits are reached. However, the significant variability in drivers' takeover time (the time needed to regain control) poses challenges in balancing time budgets to avoid being too short (compromising safety and comfort) or too long (reducing driver alertness). Prior work lacks systematic exploration of sufficient time budgets across scenarios and drivers.MethodFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 100 articles are selected for review. Review papers are analyzed to extract overarching insights on time budget design, while primary empirical studies are examined to complement and validate the findings. We systematically synthesize evidence along the takeover sequence, covering (i) drivers' required takeover time, (ii) the time budgets provided by automated driving systems, and (iii) the resulting takeover outcomes. Based on the synthesis, we further discuss how the reviewed evidence informs the design of adaptive time budgets.ResultsThis review synthesizes evidence on the takeover sequence and shows that takeover time varies significantly across drivers and driving contexts. Fixed time budgets cannot accommodate such variability. Adaptive time budgets that adjust to driver takeover demands are therefore promising. We propose an adaptive framework in which the time budget is defined as the sum of a predicted takeover time and an additional takeover buffer. To support this framework, the review identifies a qualitative inverted-U relationship among takeover time, time budget, and takeover outcomes: outcomes improve when the time budget exceeds the predicted takeover time, but deteriorate when the budget becomes unnecessarily long.ConclusionStructuring the takeover sequence clarifies trade-offs in time budget design. Adaptive approaches that estimate takeover time and allocate an appropriate takeover buffer to achieve targeted outcomes show promise yet warrant further investigation.ApplicationInsights can optimize human-vehicle interactions for safe and comfortable takeovers, thus promoting public trust and acceptance of conditionally automated driving.
ObjectiveThe study aim was to review existing research on healthcare workers' safety climate, safety knowledge, and safety motivation as predictors of workers' safety behaviors. A second aim was to assess existing evidence of multiple spillover effects from workers' safety climate and safety behaviors to patient safety climate and safety behaviors and vice versa.BackgroundResearch has focused on patient safety but mainly investigated from the perspective of healthcare staff. A broader model of safety is proposed, where safer healthcare provision is achievable if both workers and patients participate in safety.MethodA systematic literature search was conducted. A total of 15,663 articles were screened, of which 43 met the inclusion criteria.ResultsThe impact of workers' safety climate could go beyond workers' safety performance to also include patients' safety climate, as well as their safety motivation and safety participation. Patient safety participation would, in turn, affect workers' safety compliance. Such results highlight patients' central role in healthcare safety for both service providers and users.ConclusionThis review offers a framework of safety that extends from healthcare workers to patients. It also emphasizes that patient participation can help staff safety performance, reducing the chance of errors, but it needs to be proactively encouraged by healthcare staff.ApplicationsThe insights can be used to develop interventions that improve both worker and patient safety through collaboration and co-participation. The proposed framework offers ideas and practical implications for healthcare organizations, practitioners, planners and policy makers to consider.
ObjectiveThis study evaluates how explanation type in an explainable AI (XAI) human-autonomy teaming (HAT) task affects performance, workload, trust, situation awareness (SA), and preference in a dynamic, spaceflight-relevant simulator. Second, we introduce a holistic evaluation method for comparing XAI systems across multiple outcomes.BackgroundXAI aims to improve understanding, calibrate trust, and enhance performance of an HAT, but the impact of explanation type in realistic, high-taskload HAT settings remains underexplored.MethodParticipants (N=31) completed 18 trials in a dual-task simulator requiring manual rover driving while supervising an autonomous exploration agent. Participants received various combinations of global, contrastive, and deductive explanations for AI-generated routes, with incentives tied to performance.ResultsExplanation type significantly affected manual performance (p=0.0003), autonomy performance (p<0.0001), team performance (p<0.0001), workload (p<0.0001), trust (p<0.0001), and preference (p=0.001), but not SA (p=0.41). Participants preferred global and contrastive explanations, performing better with their preferred explanation (p=0.049).ConclusionExplanation type influences performance and perception in demanding HAT contexts. A standardized, multi-metric evaluation framework is essential for understanding tradeoffs in XAI design.ApplicationIn HAT tasks like space exploration where users must quickly make decisions with an AI teammate, designers must consider the explanation method for XAI explanations. Our human-centered evaluation found a contrastive + global explanation combination was the best in our HAT task across a range of performance and preference metrics.
ObjectiveTo identify eye movement patterns that are correlated with spatial disorientation (SD) events during flights in a flight simulator that induces SD.BackgroundSpatial Disorientation is one of the main causes for aviation mishaps. It can result from illusions caused by misinterpreted vestibular or visual sensory cues, leading to an incorrect perception of an aircraft's position, attitude, or motion. SD prevention is of great importance, as there is currently no objective tool to identify its occurrence.MethodEye movements of 45 participants (30 aircrew members, 15 cadets) were recorded using Tobii Pro Glasses 2 in a Gyro-IPT SD flight simulator. Illusions were either vestibular or visual. Gaze metrics such as fixations, saccades (rapid gaze shift between two points), and visits were compared between subjects who experienced SD and those who did not. Statistical analyses were conducted to identify significant differences.ResultsAmong 284 flight profiles, 136 SD occurrences were recorded (48%). During visual illusions the participants who more frequently checked the instrument panel had a higher chance of avoiding SD. In contrast, during vestibular illusions, participants who examined the head-up display (HUD) more frequently had a lower probability of SD occurrence.ConclusionMitigating SD requires distinct eye-movement strategies tailored to the illusion type. Our results suggest that to mitigate visual illusions, there is a need for greater instrument panel focus, whereas to mitigate vestibular illusions, increased HUD engagement is needed, as opposed to the current instructions.ApplicationOur findings may inform training programs to enhance performance in high-risk SD flight profiles. Additionally, results support the potential development of a real-time SD alert system for aircraft, aiming to mitigate or prevent SD-related incidents.
ObjectiveThis study investigates the effectiveness of an AI-powered interactive vehicle owner's manual compared to a traditional static manual in improving users' understanding of Advanced Driver Assistance Systems (ADAS) using a production vehicle's owner's manual.BackgroundAs vehicle automation becomes increasingly complex, drivers face challenges in understanding ADAS features. While traditional owner's manuals have demonstrated effectiveness when they are used, there remains potential to enhance driver engagement through AI and provide more interactive and accessible learning experiences.MethodsUsing a between-subjects design, 38 participants were randomly assigned to learn about four commercially available ADAS features using either a PDF manual or a Retrieval-Augmented Generation (RAG) AI manual. Mental model accuracy was assessed through multiple-choice questions, while participants' reasoning patterns were analyzed using structural topic modeling (STM) of open-ended responses.ResultsBoth training methods improved mental model accuracy from pre- to post-training, with no significant differences between PDF and RAG conditions in quantitative learning outcomes. However, STM analysis revealed distinct qualitative differences in the participants' reasoning patterns. RAG-trained participants demonstrated more sophisticated systems-level thinking, particularly in feature integration reasoning. Analysis through the lens of the Technology Acceptance Model revealed that both methods operate through similar psychological mechanisms, with perceived usefulness aiding user acceptance.ConclusionAI-augmented owner's manuals achieve comparable learning effectiveness to traditional documentation while enhancing feature integration reasoning. Interactive AI systems serve as effective enhancements rather than replacements for proven educational approaches, guiding users toward more sophisticated mental models of complex automated systems.ApplicationThis research provides insights for automotive manufacturers and documentation specialists on effective approaches for educating drivers about complex vehicle automation systems, potentially improving safety and user experience.
ObjectiveTo investigate muscle activation and movement behavior in the lower back and legs during selected explosive ordnance disposal (EOD) operational tasks to evaluate the impact of EOD personal protective equipment (PPE) on performance and the body.BackgroundEOD PPE is designed to shield technicians from blast-related threats. However, the protective features make the PPE heavy, and restrictive to movement. It remains unclear how wearers adapt their movement under these constraints.MethodSixteen participants performed six EOD operational tasks ranked the most challenging and frequently encountered. Electromyography (EMG), force plates, and motion capture system were used to measure the movement while the tasks were performed with and without wearing EOD PPE.ResultsEMG results show task-dependent, asymmetrical muscle activation, with greater demands during physically intensive tasks such as bending, lifting, and stair climbing. A trend of decreased ankle muscle activation was observed when carrying a heavy load. Reduced center of pressure displacement was noted during tasks such as bending and rising from kneeling.ConclusionWearing EOD PPE amplifies muscle demands, particularly in the thigh muscles during movements requiring postural adjustment and lifting and may increase stiffness or restrict movement. Elevated thigh muscle activity across tasks, together with restricted mobility, increases the hip's vulnerability to overuse and musculoskeletal injury.ApplicationAltered motor behavior from wearing EOD PPE has implications for other PPE uses. Understanding human motor adaptation during operations supports prevention strategies such as exoskeletons, ergonomic designs, and targeted training, to reduce musculoskeletal strain, sustain performance, and protect long-term health.
