Most research on student AI use asks who adopts these tools, treating AI use as a stable personal characteristic. Yet each time students encounter a problem, they face a fresh decision about whether to seek AI help. We examined when students choose AI assistance by analyzing both stable individual differences and moment-to-moment decisions. Undergraduate students (N = 398) completed 30 quiz trials, freely choosing on each trial to answer independently, consult an AI chatbot, or rely entirely on AI. Before each decision, students rated their topic expertise. We also measured cognitive abilities, AI attitudes, and demographics. Multilevel modeling revealed that almost half the variance in AI use reflected stable individual differences; the other half varied within individuals across trials. Self-assessed expertise accounted for this within-person variation: students sought AI help primarily when they felt unknowledgeable about a topic. At the between-person level, fluid intelligence predicted more chatbot consultation but less full delegation, suggesting higher-ability students use AI strategically rather than as a substitute for thinking. Male students and those with favorable AI attitudes showed greater willingness to delegate entirely. These findings reframe AI use as adaptive help-seeking rather than a fixed trait. Educational interventions that target metacognitive calibration, helping students accurately judge when they need assistance, may prove more effective than blanket AI policies that assume uniform use patterns.

Human factors psychology and industrial-organizational psychology researchers carried out intensive field research investigating how future Army leaders interacted with robot teammates during large-scale, realistic military training exercises. Despite careful planning, unexpected challenges such as extreme weather and shifting schedules forced the team to adapt quickly. These researchers share their lessons learned and offer practical advice for designing flexible field research, building adaptable research teams, and testing technologies in realistic environments.

Generative AI (GenAI) systems provide complete cognitive task outputs to untrained users across unrestricted domains at population scale, creating an unprecedented form of automation. We apply Parasuraman et al.’s (2000) framework of four cognitive automation stages to characterize this shift. GenAI automates all four stages simultaneously: information acquisition, information analysis, decision selection, and action specification. Classical automation effects (e.g., the “lumberjack effect”) were established exclusively in trained operators working within bounded domains. Whether these effects persist, intensify, or transform when GenAI violates all previous scope conditions simultaneously remains an open empirical question. Nearly a billion people now use this technology without the empirical foundation needed to predict human performance consequences. This commentary maps how GenAI’s deployment configuration exceeds automation theory’s empirical boundaries and identifies critical research questions to guide investigation of human performance and safety in this understudied area of automation design.

Soldiers must rapidly decide whether battlefield robots are allies or liabilities, yet most acceptance research relies solely on self-report. We adapted the Robot Implicit Association Test (R-IAT) to capture unconscious evaluations of two remotely piloted Army reconnaissance robots—a tracked Unmanned Ground Vehicle (UGV) and a dog-like quadruped (“Spot”). One hundred thirteen undergraduates completed a seven-block R-IAT, pairing the robot images with positive or negative valence words. Response latencies revealed participants held an implicit preference for Spot over the UGV. Explicit measures did not correlate with R-IAT scores, underscoring the importance of implicit tests for revealing attitudes self-reports miss. Measuring, and designing for, these biases is essential to fielding robots that earn calibrated reliance.

ObjectiveWe conducted two experiments to understand the effects of computationally diminishing reality on performance, awareness of the environment, and subjective workload.BackgroundAdvances in extended reality (XR) technologies make it possible to alter or remove auditory and visual distractions from an environment. Though distractions are known to harm performance, there is no work examining the effects of removal via XR.MethodAcross two samples, STEM graduate students and Johnson Space Center employees, the effects of reducing distraction during a novel, demanding assembly task via a form of XR (diminished reality) were compared to a full distraction control condition, studied in a virtual reality (VR) environment. In one condition, participants experienced universal attenuation of distractions. In a second condition, attenuation was context-aware: only nontask objects were made less visible and only unimportant off-task audio was eliminated.ResultsBoth experiments found subjective workload could be lowered via a Diminished reality (DR) aid. The STEM graduate student sample showed a benefit of a DR aid for performance and environment awareness; however, the sample of professionals from Johnson Space Center showed no performance differences with the DR aids. There were mixed results regarding awareness of the location of objects and events outside of the assembly task.ConclusionDR aids can have effects similar to those seen in studies that removed distractions entirely. More work is needed to understand the match between distraction removal design and task.ApplicationThese findings contribute to the development of a class of XR aids: Diminished Reality.

The emergence of Artificial Intelligence (AI) tools offers new possibilities for simplifying complex information and supporting decision-making. This study investigates how AI-simplified language and framing effects influence decision-making in scenarios involving novel military operations. Using a two (positive vs. negative framing) × two (jargon vs. AI-simplified language) between-subjects design, participants will be presented with one of two military scenarios—one involving a high-value target (HVT) and the other addressing improvised explosive device (IED) deactivation. Outcome measures include perceived desirability of the scenario, compliance, trust in AI, and cognitive workload (NASA-TLX). Drawing from prior framing studies (e.g., Tversky & Kahneman, 1981; Levin et al., 1988), we hypothesize that positively framed, AI-translated scenarios will result in higher desirability ratings, increased compliance, and lower cognitive workload compared to negatively framed or jargon-heavy versions. This research aims to inform the design of AI tools that support clear communication and user-centered decision-making aids. It highlights the importance of considering human cognition and its possible use in the design of new technologies.

People with diabetes are at risk for diabetic retinopathy (DR), a leading cause of blindness. Early treatment can preserve sight; however, screening rates are low. We utilized psychological theories of motivation in tandem with human factors tools such as heuristic evaluation and task analysis to develop interventions to improve screening rates at a single clinic. Interventions addressed the system of screening, from the patients and their choices to clinic workers, device usability, and the clinic environment. Findings showed potential solutions to the screening issue situated within the theory of planned behavior. Future steps are to refine interventions and measure effectiveness.

Utilizing advanced machinery in team environments often necessitates reliance on a leader, or “operator,” who is in charge of interfacing with technology directly on the team’s behalf. This is particularly evident in modern military missions, where teams depend on operators of robotic machinery to safely navigate dangerous tasks or hazardous terrain. The present work is part of a larger study on integrating a semi-autonomous quadruped robot into military training exercises. This analysis focused on how trust in an operator controlling Spot influenced different aspects of human-robot interaction (HRI) among the team. Operator trust was found to be positively correlated with positive perceptions of the robot, trust in and reliance on the robot, and willingness to use the robot for future exercises. Improving operator trust, thereby shifting the focus to human-human interaction, may prove an effective avenue for bolstering confidence in robotic systems.

The aim of this experiment was to examine the relationship between individual attentional control capabilities and learning from video-based lectures with differing amounts of visual stimuli. Research suggests that extraneous stimuli in lectures, such as visual instructor presence, may increase cognitive load, thus inhibiting learning. This experiment explored how lecture format (slides that filled the screen vs. a virtual classroom), instructor presence, and attentional control ability impacted learning outcomes. Participants engaged in a battery of attention tasks, watched a lecture, and were quizzed on the lecture material. Attentional ability level interacted with instructor presence, where instructor presence improved learning for individuals with poor attentional capacity but slightly harmed learning for those with better attentional capacity. There were no main effects of instructor presence or lecture format. Despite the additional stimuli that instructor presence adds to a lecture, it is possible that the speaker’s image may increase engagement.

With their increased capability, AI-based chatbots have become increasingly popular tools to help users answer complex queries. However, these chatbots may hallucinate, or generate incorrect but very plausible-sounding information, more frequently than previously thought. Thus, it is crucial to examine strategies to mitigate human susceptibility to hallucinated output. In a between-subjects experiment, participants completed a difficult quiz with assistance from either a polite or neutral-toned AI chatbot, which occasionally provided hallucinated (incorrect) information. Signal detection analysis revealed that participants interacting with polite-AI showed modestly higher sensitivity in detecting hallucinations and a more conservative response bias compared to those interacting with neutral-toned AI. While the observed effect sizes were modest, even small improvements in users' ability to detect AI hallucinations can have significant consequences, particularly in high-stakes domains or when aggregated across millions of AI interactions.

We investigated the potential for augmented reality (AR) as a training aid for spatial estimation skills. Though there are many tools to support spatial judgments, from measuring cups to rulers, not much is known about training spatial skills for retention and transfer. Display of AR was manipulated to train the spatial skill of portion estimation. In Experiment 1, an AR-aided strategy of creating smaller portions out of a larger example amount was compared to a no-AR control condition. This manipulation was based on previous non-AR experiments where amorphous foods were better estimated when divided into smaller portions. There was a significant benefit of estimating using a solid AR shape. In Experiment 2, cognitive anchoring was manipulated. Using meaningful AR anchors resulted in the best performance and most learning. We conclude that spatial estimation skills can be combined with mental strategies and trained via AR.

Objective: Discuss the human factors relevance of attention control (AC), a domain-general ability to regulate information processing functions in the service of goal-directed behavior.Background: Working memory (WM) measures appear as predictors in various applied psychology studies. However, measures of WM reflect a mixture of memory storage and controlled attention making it difficult to interpret the meaning of significant WM-task relations for human factors. In light of new research, complex task performance may be better predicted or explained with new measures of attention control rather than WM. Method: We briefly review the topic of individual differences in abilities in Human Factors. Next, we focus on WM, how it is measured, and what can be inferred from significant WM-task relations.Results: The theoretical underpinnings of attention control as a high-level factor that affects complex thought and behavior make it useful in human factors, which often study performance in complex and dynamic task environments. To facilitate research on attention control in applied settings, we discuss a validated measure of attention control that predicts more variance in complex task performance than WM. In contrast to existing measures of WM or AC, our measures of attention control only require 3 minutes each (10 minutes total) and may be less culture-bound making them suitable for use in applied settings.Conclusion: Explaining or predicting task performance relations with attention control rather than WM may have dramatically different implications for designing more specific, equitable task interfaces, or training.Application: A highly efficient ability predictor can help researchers and practitioners better understand task requirements for human factors interventions or performance prediction.

Trust remains a critical focus within human-robot interaction research with the benefits of trust including increased task performance. There is a growing body of literature that suggests that the perceived characteristics of robots play a role in trust; however, less is known about the relationship between trust and the perceived characteristics of an autonomous robot teammate in an applied military setting. We investigated the relationship between the perceived characteristics of a robot teammate and the level of trust in the robot by equipping United States Military Academy (USMA) cadets with a pseudo-autonomous quadrupedal robot teammate during field training. We found that the likability, animacy, perceived intelligence, and perceived safety of the robot positively correlated with trust.

We sought to understand how individual differences drive trust and behavior in human-robot teams when collaborating with an imperfect robot teammate. A recent review suggested a focus on individual differences to provide insight to adaptations in using future technology including robots, artificial intelligence, and autonomous systems (Matthews et al., 2020). In the current study, participants collaborated with a robot teammate to compete against two robot opponents in four games of Capture the Flag. We examined the effects of negative attitudes towards robots (personality measure), and experienced unreliability (situational measure) on performance and trust in a robot partner. Those with pre-existing negative attitudes towards robots performed more poorly and did not improve across the study. Those with the most positive attitudes toward robots improved the most. Those with high negative attitudes also reduced their trust in their robot partner as games progressed. In sum, all robotic systems will have failures. Our results showed that attitude is one of the variables crucial to predicting response to those failures and is moderated by the experience of robot failure to understand and carry out commands. Individuals with negative attitudes towards robots may need extra training or a robot partner that is more transparent in explaining the rationale for its actions or limitations in its reliability.

Diabetic Retinopathy (DR) is the main contributor to adult blindness in America. When detected on time, treatment can avoid severe sight loss 95% of the time (Fong et al., 2004). However, only 50% of people with diabetes get screened yearly, making early intervention difficult (Lee, et al., 2003). There is a need to understand how the systems for DR screenings can be designed to comply with the patient's needs, for which it is necessary to understand the user and the factors that affect their behavior. We created a questionnaire from barriers and motivators found in interviews with persons with diabetes regarding their yearly screenings (Salas, et.al., 2022) based on Ajzen’s (2006a) Constructing a Theory of Planned Behavior Questionnaire. The questionnaire measured the influence of attitudes, social norms, and perceived control on screening for DR. This study will add to the current body of literature by helping to identify where to focus efforts when creating systems for DR screening.

Coordination of the necessary efforts of medical personnel, caregivers, and social networks to support a patient with a chronic health condition increases time consumption and costs. According to a CDC study from 2023, six out of ten Americans suffer from chronic illnesses, including diabetes, which can lead to other medical complications like diabetic retinopathy (DR). Care coordination programs are one of the systems currently in use to assist in the management of patient’s healthcare and the network of individuals involved in their treatment plans. Compared to institutions that utilize fewer care coordination systems, those that use these programs consistently have much higher patient attendance rates. Therefore, it is important that to improve the current systems we comprehend the user experience with care coordination. To study the barriers and motivations underlying participation in care coordination programs among diabetes patients, we created an interview utilizing the Integrated Behavior Model (IBM). The findings from our interviews will contribute to the body of existing literature by identifying barriers and motivators that must be taken into consideration when designing DR screening system aids.

Although previous studies have developed scales to measure levels of robot autonomy and ways to quantify trust in human-robot teams, there is still much interest in determining how trust, individual differences, and levels of autonomy impact these teams. The primary objective of this study is to identify how pre-existing attitudes about robots, individual differences in normal cognition, and levels of robot autonomy affect performance, communication, and trust with a robot teammate. This study pairs participants with a robot teammate to compete against two robotic opponents in a simulated game of capture the flag. Game performance and interactions with the robot teammate will be collected as outcome measures. Subjective measures will include pre-existing negative attitudes toward robots (Nomura et al., 2006a) and trust (Schaefer, 2016).

Precipitated by the COVID-19 pandemic, many work systems—including those focused on research activities—have transitioned to remote operations and many may remain remote even after in-person operations no longer present a public health risk. The ability to conduct human subjects research remotely presents many benefits but also numerous challenges. This panel gathers experts in the design, adaptation, and performance of remote macroergonomics research. They will describe their remote methodologies, and evaluate past, current, and future work in this area.

Robots are increasingly utilized to work with humans in collaborative tasks. While there is a growing body of research investigating individual measures that impact human-robot interaction (HRI), to our knowledge, no measure exists to quantify an individual's perception of robot power. How powerful one perceives robots could be a driving factor in an individual's attitudes toward robots and their trust in HRI. This study aims to develop and validate a scale to quantify peoples' general perception of the power of robots. Preliminary results from exploratory factor analyses with nearly 60% of planned participants revealed three potential factors: companion/task robots, social coworking robots, and domineering robots. Future work will examine whether and how the scale predicts behavior to continue to refine the scale and isolate its measurement.