Modern interactions with technology are increasingly moving away from simple human use of computers as tools to the establishment of human relationships with autonomous entities that carry out actions on our behalf. In a recent commentary, Peter Hancock issued a stark warning to the field of human factors that attention must be focused on the appropriate design of a new class of technology: highly autonomous systems. In this article, we heed the warning and propose a human-centred approach directly aimed at ensuring that future human–autonomy interactions remain focused on the user’s needs and preferences. By adapting literature from industrial psychology, we propose a framework to infuse a unique human-like ability, building and actively repairing trust, into autonomous systems. We conclude by proposing a model to guide the design of future autonomy and a research agenda to explore current challenges in repairing trust between humans and autonomous systems. Practitioner Summary: This paper is a call to practitioners to re-cast our connection to technology as akin to a relationship between two humans rather than between a human and their tools. To that end, designing autonomy with trust repair abilities will ensure future technology maintains and repairs relationships with their human partners.

This study examined student experiences before and after an essay writing assignment that required the use of ChatGPT within an undergraduate engineering course. Utilizing a pre-post study design, we gathered data from 24 participants to evaluate ChatGPT's support for both completing and grading an essay assignment, exploring its educational value and impact on the learning process. Our quantitative and thematic analyses uncovered that ChatGPT did not simplify the writing process. Instead, the tool transformed the student learning experience yielding mixed responses. Participants reported finding ChatGPT valuable for learning, and their comfort with its ethical and benevolent aspects increased post-use. Concerns with ChatGPT included poor accuracy and limited feedback on the confidence of its output. Students preferred instructors to use ChatGPT to help grade their assignments, with appropriate oversight. They did not trust ChatGPT to grade by itself. Student views of ChatGPT evolved from a perceived “cheating tool” to a collaborative resource that requires human oversight and calibrated trust. Implications for writing, education, and trust in AI are discussed.

In many emerging civilian and military operations, human operators are increasingly being tasked to supervise multiple robotic uninhabited vehicles (UVs) with the support of automation. As 100% automation reliability cannot be assured, it is important to understand the effects of automation imperfection on performance. In addition, adaptive aiding may help counter any adverse effects of static (fixed) automation. Using a high-fidelity multi-UV simulation involving both air and ground vehicles, two experiments examined the effects of automation reliability and adaptive automation on human-system performance with different levels of task load. In Experiment 1, participants performed a reconnaissance mission while assisted with an automatic target recognition (ATR) system whose reliability was low, medium, or high. Overall human-robot team performance was higher than with either human or ATR performance alone. In Experiment 2, participants performed a similar reconnaissance mission with no ATR, static automation, or with adaptive automation keyed to task load. Participant trust and self-confidence were higher and workload was lower for adaptive automation compared with the other conditions. The results show that human-robot teams can benefit from imperfect static automation even in high task load conditions and that adaptive automation can provide additional benefits in trust and workload.

Research suggests that humans and autonomous agents can be more effective when working together as a combined unit rather than as individual entities. However, most research has focused on autonomous agent design characteristics while ignoring the importance of social interactions and team dynamics. Two experiments examined how the perception of teamwork among human–human and human–autonomous agents and the application of team building interventions could enhance teamwork outcomes. Participants collaborated with either a human or an autonomous agent. In the first experiment, it was revealed that manipulating team structure by considering your human and autonomous partner as a teammate rather than a tool can increase affect and behavior, but does not benefit performance. In the second experiment, participants completed goal setting and role clarification (team building) with their teammate prior to task performance. Team building interventions led to significant improvements for all teamwork outcomes, including performance. Across both studies, participants communicated more substantially with human partners than they did with autonomous partners. Taken together, these findings suggest that social interactions between humans and autonomous teammates should be an important design consideration and that particular attention should be given to team building interventions to improve affect, behavior, and performance.