We first present a model of the influence of four properties of automation on human-automation team performance, dichotomized between conditions when automation functions as designed and when automation fails. These properties are reliability, transparency, degree of automation, and concurrent task workload during automation use. Mediating cognitive variables between properties and performance are trust, dependence, situation awareness, and mental workload. The strength of these influences is estimated from effect sizes in meta-analyses. We then present a meta-analysis of the effect of concurrent task workload on automation failure response as a function of non-driving related task (NDRT) workload within the context of driver take-over request time. Results from 11 studies revealed no overall penalties (mean effect size) for workload on failure response but found considerable variability across studies. Possible moderator variables, including task engagement are presented. These results are interpreted in terms of a model of optimal resource allocation between monitoring automation and the NDRT.
BART (Bay Area Rapid Transit) serves commuters, students, and visitors in the Bay Area. However, ridership has declined due to rising crimes, drug use, sexual assaults, and homelessness. These challenges disproportionately impact women and minorities, many of whom report discomfort, distrust in the system, and negative safety perceptions (Alliance for Girls Youth, 2024). The goal is to examine how safety perception influences BART riders’ experiences and decision making through the lenses of Human Factors and Industrial Engineering. We conducted a mixed-method research to understand perceived risk, environmental awareness, and support riders’ needs when commuting. Participants, particularly women, had reported feeling unsafe at night, traveling in groups, strategically choosing seatings, and staying vigilant in fear of being harmed. Riders also expressed interest in a verified “buddy feature,” safety alerts, and reporting incidents. Overall, this highlights opportunities to improve safety, build trust, and improve the commuting experience for BART.
Augmented reality (AR) technologies are increasingly being explored as tools for advancing hearing assistance, offering the potential to supplement or extend the capabilities of traditional hearing aids. From a human factors perspective, this systematic review evaluates the current state of AR-integrated audio systems and their impact on communication, accessibility, spatial awareness, cognitive workload, and user adaptability in everyday environments. Using PRISMA guidelines, a search of PubMed and Engineering Village (Compendex and Inspec) identified 477 articles published between 2000 and 2025, of which fourteen met inclusion criteria. Findings across these studies were characterized into four areas: personalization and accessibility, ecological validity, multimodal sensing, and spatial/3D audio modeling. Results indicate that AR-assisted devices show promise in supporting user-driven auditory adjustments, enhancing spatial awareness, and enabling more realistic testing environments through AR/VR soundscapes. However, many solutions remain early prototypes, with limited evaluation in naturalistic settings and sparse reporting on long-term user experience. Overall, AR audio systems demonstrate significant potential to reshape hearing-aid design, but further longitudinal and real-world research is required to validate their impact on listening performance and everyday communication.
AI systems are increasingly common in decision-making tasks. However, these systems often generate probabilistic responses, resulting in uncertainty in their outputs. Understanding this uncertainty can help users make robust decisions, such as when to trust the agent and whether to seek more information. Interpreting uncertainty is not always straightforward, though; past work shows there is variability in how people interpret probabilistic information (e.g., weather forecasts). This study builds on that research by analyzing how people interpret probabilistic AI responses. In this survey-based experiment ( N = 632), participants interpreted AI uncertainty statements. The word used to describe the uncertainty (confidence, certainty, reliability, or accuracy) varied between trials. Participants often treated confidence and certainty as indicators of how sure the agent was about its output, whereas interpretations of reliability and accuracy were more variable. These findings can guide how AI systems communicate uncertainty, supporting more accurate user interpretations and robust decision-making.
Mobility disabilities are among the most common disability types in the U.S., where the need to design safe powered mobility devices is increasingly important for user safety. For power wheelchair users, AI-enabled solutions have been introduced to avoid collisions. However, as control shifts from the user to automation, systems need to communicate intent clearly and maintain user trust. Auditory verbal cues can support decision-making: explanations may emphasize action-focused maneuvers or why-focused rationales. Tactile displays can also support communication through vibrations presented to the back or wrist. However, because safety-critical messages based on AI-inferred intent have not been systematically investigated and because power wheelchair users experience a different embodied interaction, there remains an opportunity to understand these factors. This pilot study explored the effects of communication strategy (action-focused, why-focused) and vibrotactile feedback location (seatback, wrist) on wheelchair performance and user perceptions. Six able-bodied participants completed wheelchair driving sessions while receiving obstacle alerts. Results indicated that seatback vibrations were associated with higher satisfaction ratings than wrist vibrations. No significant main effects were found for reaction time, decision-making accuracy, or usefulness ratings. These data can inform future studies designing intelligent wheelchair interfaces.
