Artificial intelligence, extended reality, and emerging AI–XR integrations in medical education

Talia Tene^1*Diego Fabián Vique López²Marlene Jacqueline García Veloz²Byron Stalin Rojas Oviedo²Richard Tene-Fernandez³

• ¹Universidad Técnica Particular de Loja, Loja, Ecuador
• ²Escuela Superior Politecnica de Chimborazo, Riobamba, Ecuador
• ³Hospital Metropolitano, Quito, Ecuador

Abstract Introduction: Artificial intelligence (AI) and extended reality (XR)—including virtual, augmented, and mixed reality—are increasingly adopted in health-professions education. However, the educational impact of AI, XR, and especially their combined use within integrated AI–XR ecosystems remains incompletely characterized. Objective: To synthesize empirical evidence on educational outcomes and implementation considerations for AI-, XR-, and combined AI–XR–based interventions in medical and health-professions education. Methods: Following PRISMA and PICO guidance, we searched three databases (Scopus, PubMed, IEEE Xplore) and screened records using predefined eligibility criteria targeting empirical evaluations in health-professions education. After deduplication (336 records removed) and two-stage screening, 13 studies published between 2019 and 2024 were included. Data were extracted on learner population, clinical domain, AI/XR modality, comparators, outcomes, and This is a provisional file, not the final typeset article implementation factors, and narratively synthesized due to heterogeneity in designs and measures. Results: The 13 included studies involved undergraduate and postgraduate learners in areas such as procedural training, clinical decision-making, and communication skills. Only a minority explicitly integrated AI with XR within the same intervention; most evaluated AI-based or XR-based approaches in isolation. Across this mixed body of work, studies more often than not reported gains in at least one outcome—knowledge or skills performance, task accuracy, procedural time, or learner engagement— relative to conventional instruction, alongside generally high acceptability. Recurrent constraints included costs, technical reliability, usability, faculty readiness, digital literacy, and data privacy and ethics concerns. Conclusions: Current evidence on AI, XR, and emerging AI–XR integrations suggests promising but preliminary benefits for learning and performance. The small number of fully integrated AI–XR interventions and the methodological limitations of many primary studies substantially limit the certainty and generalizability of these findings. Future research should use more rigorous and standardized designs, explicitly compare AI-only, XR-only, and AI–XR hybrid approaches, and be coupled with faculty development, robust technical support, and alignment with competency-based assessment.