Adaptive just-in-time training supports skill retention in virtual reality simulations

Abstract

Effective training systems must be designed to support not only the mastery of trained skills, but also retention, especially in high consequence industries. As trainee time is finite, it is critical to optimize parameters of frequency, spacing, and content facilitation to enable efficiently retaining skills. These constraints have driven the growth of just-in-time (JIT) training approaches, with targeted reinforcement of skills just prior to evaluation. In this study, we implement and assess a virtual reality (VR) adaptive difficulty facilitation algorithm for providing personalized skill retention training. We also investigate the cadence of refresher training. N = 31 subjects trained to proficiency in a VR spacecraft piloting simulation task. They were then assigned to one of three retention conditions: no training for a 5-week period, once-per-week training with an adaptive difficulty algorithm, or a single JIT training session prior to evaluation. Final evaluation performance was affected by training condition (p = 0.02), with better retention performance with JIT training than with no training (p = 0.018). In contrast, the subjects in the weekly trained condition may suffer from overtraining at high difficulty levels, with a negative correlation in a post hoc analysis between difficulty level achieved and final evaluation performance (p = 0.02). Our assessments of flow, motivation, preparation, and confidence were not significantly impacted by retention training condition. The results of this study demonstrate the value of personalized, adaptive JIT in VR for retaining training and mitigating skill loss in a virtual reality simulation environment.