Designing Adaptive Extended Reality Training Systems Based on Expert Instructor Behaviors

Advances in training technologies using Extended Reality (XR) offer dramatic improvements in terms of time and associated costs over traditional training methods. Industries that are reliant on human labor are starting to embrace XR devices such as Virtual and Augmented Reality (VR/AR) Head-Mounted Displays (HMDs) to capitalize on these advantages. Additionally, developers now have the capacity to create customized user experiences and reduce instructor workload by employing adaptive automation in XR training simulations. Adaptive systems analyze the behavior of a learner for a trigger and then deploy an adaptation to alter the system state. While examples of adaptive XR training systems in current literature show that they are feasible to develop, further research must be done to quantify their efficacy. Furthermore, recommendations for how to design triggers and adaptations for XR training simulations are currently non-existent, resulting in negative impacts to training if inappropriate adaptations are implemented. This paper provides novel, evidence-based recommendations for the design of future adaptive XR training systems by learning from expert XR instructors. To this end, semi-structured interviews were conducted with 11 XR trainers. Participants were asked to discuss their experiences dealing with learners who exhibited confusion during XR training. Interviews were analyzed for existing and emerging themes. Finally, these themes were applied to existing trigger and adaptation models and synthesized into design recommendations for XR training developers. The outcomes of this work will inform the future development of adaptive XR training platforms.