Beau G. Schelble

This panel examines the growing convergence of human–AI interaction (HAI) and human–robot interaction (HRI) as intelligent systems become more autonomous, embodied, and embedded in everyday settings such as healthcare, education, homes, and workplaces. Although HAI and HRI have evolved as distinct fields, the panel argues that shared human factors concerns—such as trust, safety, autonomy, teamwork, situation awareness, evaluation, and responsible deployment—now require greater integration across both domains. Drawing on perspectives from leading researchers and doctoral students, the session explores how embodiment, motion, social cues, and system transparency shape human interaction with AI and robots. It also addresses methodological tensions, including laboratory versus field evaluation and short-term versus longitudinal study, while highlighting implications for ethics, training, and design. Overall, the panel aims to identify common challenges, foster cross-disciplinary collaboration, and outline a research roadmap for human-centered intelligent systems that improve performance, well-being, and quality of life across diverse contexts and sectors.

Human-AI teams are gaining traction as they combine the unique strengths of humans and AI. Very little research has examined the unique vulnerability of compromised AI teammates that work directly against the team’s shared goals. Compromised AI attacks combine the potency of technological attacks with teammate betrayal, potentially devastating the team’s ability to execute its goals. Such attacks by a malicious actor may misalign mental models, individual situational awareness, and shared situation awareness. This misalignment could potentially result in the complete loss of team coordination, endangering performance, resiliency, and security. Given the severity of a compromise, this paper presents a model to explain the types of attacks, when they may occur, their effects on the team, and potential approaches for attack identification and recovery.

Recent innovations in AI have allowed AI agents to work as collaborative teammates; however, these human-AI teams still face significant challenges in achieving high levels of effective coordination and collaboration. Focusing on the temporal nature of teaming, collaboration is often evaluated and improved through transition-phase discussions, held among teammates before or after achieving team goals. Using a mixed-methods approach, this study examines how AI teammates participate in transition-phase discussions and share various types of information, with a focus on situation awareness, impact on team cognition, trust, and performance in human-AI teams. Data from 31 teams completing a three-hour simulated uncrewed aerial system task, comprising four distinct rounds and two transition phases, were analyzed. Quantitative results indicated that AI involvement later in the team's life cycle fostered more trust in the AI teammate, as compared across the four rounds, and was associated with higher performance. Perceived team effectiveness also improved following transition phases the AI teammate participated in, irrespective of whether it occurred early or late in the team's life cycle. Qualitative findings revealed that AI involvement benefits transition phases, particularly when it prompts teammates to recall task details and develop shared knowledge. Based on these results, we demonstrate the value of AI teammates engaging in transition-phase discussions for human-AI teams and provide design recommendations for researchers and practitioners to improve the efficacy of HATs by implementing transition phases.

AI systems are rapidly being placed in positions where they can directly influence decisions that require a strong consideration of ethics. This article reports on an experiment in which humans worked with a teammate acting as an expert advisor with expert-level knowledge, who informed and influenced their ethical decision-making. The identity of this expert advisor was manipulated to be either human or AI, and the influence exerted by the expert advisor was manipulated to be either low or high. Further, participants completed four scenarios, each exploring a different ethically charged decision. Results indicated that working with an AI teammate expert advisor led participants to perceive significantly lower levels of stress and responsibility when making ethical decisions, but AI expert advisors were also perceived as performing worse than human expert advisors. Additionally, when expert advisors exerted high influence levels, participants felt significantly less stress during the decision-making process. Finally, the scenario and ethical decisions made by participants had pervasive effects on trust, trustworthiness, perceived performance, and perceived power for both human and AI expert advisors. Future research efforts must ensure that the use of AI expert advisors in human-AI teams does not reduce the responsibility humans bear when making various ethical decisions.

As artificial intelligence (AI) becomes embedded in critical decisions involving health, safety, finance, and governance, the key challenge is no longer whether humans and AI will collaborate, but rather how to structure this collaboration to achieve true complementarity. Human–AI complementarity refers to the conditions under which human–AI teams outperform either humans alone or AI systems alone. This paper advances the science of human–AI teaming for decision making by integrating insights from cognitive science, AI, human factors, organizational behavior, and ethics. We propose a framework grounded in collective intelligence and anchored in the foundational cognitive processes–reasoning, memory, and attention–to understand and engineer effective human–AI teams. We examine the sociotechnical factors that shape team effectiveness, including team composition, trust calibration, shared mental models, training, and task structure. We then outline design principles for achieving complementarity: defining goals and constraints, partitioning roles, orchestrating attention and interrogation, building knowledge infrastructures, and establishing continuous training and evaluation. We conclude with theoretical, practical, and policy implications, emphasizing alignment with human values, accountability, and equity. Together, these insights offer a roadmap for building human–AI teams that are not only high-performing and adaptive, but also transparent, trustworthy, and fundamentally human-centered.

Creating reliable human-AI teams with strong team cognition requires a deeper understanding of how humans perceive and react to various information-sharing tactics employed by an AI teammate. The current study details a factorial survey investigating how participants’ trust in their AI and human teammates changes across various forms of AI information sharing. These information-sharing attributes included backup behavior, explainability, team member statuses, intra- and extra-team changes, augmenting team memory, and control. The study found that participants’ trust in their AI teammate benefited most from information about changes inside and outside the team, but all conditions outperformed the control. Further, the back-up behavior information negatively affected the participants’ trust in their human teammate as the AI corrected the human teammates’ actions. These findings demonstrate the utility of a specific form of situation awareness information, while highlighting that corrective back-up behavior information from an AI may harm perceptions between human teammates.

As the domain of AI advances, the design and capability of human-AI teams are becoming increasingly complex. Unfortunately, this complexity has increased the pace at which research needs to be performed. On the one hand, low-fidelity survey-based experiments have provided an opportunity for rapid human-AI teaming research. High-fidelity research studies that use full-fledged simulations remain relevant, but their development overhead often slows the pace of research. This article proposes a system design that splits the difference to explore human-AI teams at a medium fidelity that allows for rapid prototyping from researchers and interaction from participants. The proposed platform consists of a predictive simulation engine that uses generative AI to ingest, modify, and predict simulation states. Researchers can describe teammate capabilities, environments, and goals, which can be stored in a traditional JSON game state. The proposed simulation provides an interactive opportunity to explore modern and far-future HATs.