AI-Supported XR Training: Personalizing Medical First Responder Training

Extended Reality (XR) technologies have become an increasingly popular tool for training medical first responders (MFR) and simulation of mass casualty incidents (MCI). The possibility of training scenarios that would be highly complex and potentially dangerous in reality provides a considerable opportunity for adopting this technology. However, the level of automatization of performance monitoring and personalization of training content is still a challenge that has not been solved. This paper introduces an innovative AI-supported XR training approach to address the challenges faced by current simulation training solutions. The proposed approach incorporates wearable and sensor technology to collect physiological metrics and utilizes machine learning algorithms to identify stressors and key performance indicators (KPIs) unique to each trainee. The collected data is then analysed and translated into personalized recommendations for scenario adaptation. We discuss the advantages and challenges encountered during the development of this framework and propose methodologies for its implementation and evaluation.