A hierarchical adaptation framework for adaptive training systems

Abstract

Real-time adaptation is challenging in both operational and training environments, as the system must be able to identify what, why, and when mitigation is needed, and how best to mitigate to optimize the human-system interaction. Training systems have additional complexities, as the sole goal is not to optimize performance as in operational environments, but to optimize training, which may involve more error allowance for learning opportunities. This paper outlines a proposed hierarchical adaptation framework for adaptive training systems, involving diagnoses of learning state, performance, and expertise. It will also discuss candidate approaches to obtaining the necessary measurements using physiological and neurophysiological processes, provide some guidance for designing strategies for optimal adaptation, and highlight current challenges and future research areas. © 2011 Springer-Verlag.