Immersion, realism and engagement: exploring the impact of generative AI on virtual training performance across sectors

Purpose The purpose of this study is to investigate the psychological and performance-based outcomes of generative artificial intelligence (AI)-powered virtual simulations. Specifically, this study examines the mediating role of user engagement in the relationship between immersion, perceived realism and training performance in medical, military and industrial domains. Design/methodology/approach A mixed-methods approach was used, combining quantitative survey data, performance metrics and qualitative interviews. In all, 90 participants from three different sectors finished simulated challenges created by AI. The links between immersion, realism, engagement and performance were postulated and tested using structural equation modeling or SEM. The measurement model was confirmed using confirmatory factor analysis (CFA), and sector-specific differences were evaluated using multi-group SEM. Findings The SEM analysis revealed that immersion (β = 0.52 and p < 0.001) and realism (β = 0.35 and p < 0.01) significantly predicted user engagement, which in turn positively affected training performance (β = 0.48 and p < 0.001). The results of this study highlight that the relationship between simulation design elements and performance outcomes was totally mediated by engagement. The medical and military groups exhibited the strongest effects, according to sector-specific study, whereas industrial training revealed relatively lesser engagement–performance linkages. Practical implications By illustrating how generative AI may revolutionize virtual training environments to improve workforce development across high-risk and precision-dependent industries like health care, defense and industrial operations, this study has important implications for engineering management. This study demonstrates through empirical data based on cognitive and behavioral science that realistic and immersive AI-driven simulations greatly increase user engagement, which is a necessary precondition for better performance results. These results provide engineering managers with useful information for creating and executing training initiatives that are more efficient, scalable and effective. A strategic roadmap for customizing simulation design to domain-specific requirements is also provided by the sector-specific analysis, which maximizes learning results while lowering operational risk. This study contributes to policy decisions on technology adoption, talent development and human factors integration in engineering contexts by emphasizing user engagement as a performance driver. This, in turn, improves organizational resilience and innovation capability. Originality/value This study aims to empirically investigate how immersion and realism affect engagement and performance in virtual training by combining SEM, generative AI and psychological theories in a novel way. Its user-centered approach and cross-sector analysis provide unique insights into enhancing AI-driven simulations for a range of professional fields.