A Unified Predictive Framework for Human-AI Co-Adaptation, Cognitive Workload, Trust Dynamics, and Delay-Resilient Autonomy in Long-Duration Space Missions

Long-duration human spaceflight presents complex challenges that extend beyond current human-autonomy paradigms. This paper introduces a fully integrated predictive framework that combines four interrelated components-human-AI co-adaptation, predictive cognitive workload management, trust dynamics modeling, and delay-resilient contextual autonomy-to address critical gaps in autonomous systems for space exploration. Unlike existing approaches that consider these dimensions in isolation, the proposed framework operates as a cohesive system, enabling real-time adaptation between human operators and autonomous agents, anticipating cognitive overload before errors occur, quantifying and responding to evolving trust relationships, and supporting robust autonomous decision-making under extended communication delays. The framework is demonstrated through synthetic mission simulations that illustrate its potential to enhance task performance, sustain effective human-AI collaboration, and maintain operational stability in environments with significant communication latency. Human-AI co-adaptation allows autonomous systems to learn and adjust based on individual behavioral patterns, while predictive workload modeling identifies emerging cognitive strain using task and performance indicators. Trust dynamics modeling offers a quantified approach to maintaining appropriate reliance on automation, and delay-resilient autonomy supports mission continuity when Earth-in-the-loop guidance is constrained. This work presents a novel and actionable pathway for advancing human-autonomy systems in future long-duration missions, contributing to a deeper understanding of integrated human-AI behavior and offering insights directly relevant to mission design and autonomous operations research.