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Human activity is increasingly extending into environments marked by isolation, confinement, and extreme conditions, including long-duration space missions, polar research stations, intensive care units, and other high-risk settings. In these contexts, individuals must sustain performance and wellbeing under persistent cognitive, emotional, and social strain. Artificial intelligence (AI) systems are now deeply embedded in such environments, supporting decision making, monitoring, training, and, in some cases, psychological wellbeing. Yet research on AI in extreme settings has largely emphasized technical performance and automation, with comparatively limited attention to the lived experience of sustained Human–AI Interaction (HAI). This perspective paper argues that isolated, confined, or extreme (ICE) environments represent a uniquely revealing context for examining HAI. The psychological pressures characteristic of ICE settings—such as prolonged isolation, cognitive fatigue, stress, and high consequences of error—fundamentally shape how humans perceive, trust, and rely on AI systems. Drawing on interdisciplinary literature from human factors, psychology, and AI research, the paper conceptualizes ICE environments as a stress test for HAI, where issues of trust calibration, autonomy, transparency, and social attribution are amplified. Rather than treating AI solely as a decision aid, this perspective highlights how AI systems in ICE contexts may function as cognitive partners, social surrogates, or perceived teammates. The paper concludes by outlining key implications for the design, evaluation, and governance of AI systems intended for extreme environments, emphasizing the need for interaction-centered approaches that prioritize human experience alongside technical performance.
Osei et al. (Fri,) studied this question.