Evaluating AI-driven characters in extended reality (XR) healthcare simulations: A systematic review.

Abstract

AI-driven characters in extended reality (XR) healthcare simulations are increasingly used for clinical training, yet their effectiveness, implementation, and quality assurance remain poorly understood. We conducted a systematic review of 132 studies published between January 2015 and July 2025, including 11 randomized controlled trials (RCTs), sourced from biomedical, computing, and education databases and targeted proceedings. Most studies used virtual reality (62.1%) and focused on effectiveness (n = 71), with fewer examining implementation (n = 45) or quality assurance (n = 44). Meta-analysis of two RCTs found a large effect on knowledge and decision-making (Hedges’ g = 1.31, 95% CI 0.08–2.54, = 85%), while one RCT reported faster task performance with AI-driven characters (g = -0.68, 95% CI -1.32 to -0.04). Certainty of evidence was low due to small samples and high heterogeneity. Implementation success was often associated with phased roll-outs and faculty training, but quality assurance practices (particularly bias audits and transparency measures) were rarely documented. The review proposes the DASEX framework to address these gaps and guide future integration of AI-driven characters in XR training.