A Scenario Generation Framework for Targeting Emotional Vulnerabilities in Virtual Reality First Responder Training

This paper proposes a novel approach for the dynamic generation of training scenarios in Virtual Reality (VR), in the context of first-responders training. Our proposed model leverages an ontology, Large Language Models (LLMs) and Fuzzy Cognitive Maps (FCM) in order to produce scenarios meant to train first responders emotion regulation skills to be better prepared for complex, high-risk situations. These scenarios take into account users’ personal profiles, containing notably individual emotional vulnerabilities, i.e. which situational elements are susceptible to stress them more than others, in order to produce personalized, effective training experiences. The Fuzzy Cognitive Maps allow the system to model a degree of uncertainty when choosing story events, with probabilities for choosing the next scenario event being influenced by several factors, mainly the user’s profile and the virtual world state. Our evaluation shows that for randomized initial situations and user profiles, our system generates varied story events, offering diversity in generated scenarios.