Artificial intelligence-based clinical simulation and its contribution to learning and cognitive processes in undergraduate medical students: a scoping review

Introduction: The training of future physicians occurs in a context where preventable clinical errors are still frequent and safe decision-making is increasingly complex. Faced with this scenario, clinical simulation has emerged as a key strategy for risk-free learning for patients. More recently, the incorporation of artificial intelligence has begun to transform these simulated environments, allowing for more interactive, adaptive, and personalized experiences. However, the real impact of Artificial intelligence-based clinical simulation on learning outcomes and cognitive processes in undergraduate medical students has not yet been clearly systematized. Methodology: A scoping review will be carried out to analyze scientific articles, educational studies, reviews, and technical literature published between 2015 and 2025, which address the use of Artificial intelligence-based clinical simulation in medical education. The search will be carried out in the PubMed, Scopus, Web of Science and BIREME databases, using MeSH and DeCS descriptors related to artificial intelligence, clinical simulation, learning and medical education, complemented by the review of grey literature and the chain search technique of references. Expected results: It is expected to map the current applications of Artificial intelligence-based clinical simulation in undergraduate medical education, as well as the learning outcomes, cognitive processes, benefits, challenges, and knowledge gaps reported. Conclusions: This review will offer a comprehensive view of the potential and limitations of clinical simulation based on artificial intelligence, contributing to guide its critical and pedagogically grounded incorporation into undergraduate medical training. Descriptors (DeCS/MeSH): Artificial Intelligence; Clinical Simulation; Medical Education; Medical Students; Learning. Keywords: Clinical simulation; artificial intelligence; undergraduate medical education; clinical reasoning; learning.