Personalised Training: Integrating Recommender Systems in XR Training Platforms

Abstract

The fast-paced growth of Extended Reality (XR) technologies in complex environments, such as training scenarios, has highlighted the need to implement Artificial Intelligence (AI) modules in the simulations to support trainers and trainees in these unfamiliar contexts. Among the possible AI solutions, recommender systems (RS) could be used to improve the users’ interactions and experience in immersive training environments. This work describes the integration of a RS in the framework of an XR training platform and how the design of interfaces to present recommendations can maximize acceptance of the suggestions in hybrid human-intelligent systems. By allowing trainers to adapt training scenarios during the execution of the exercise, successful and personalized training goals can be achieved.