Integration of Artificial Intelligence in Nursing Simulation Education

BACKGROUND: Artificial intelligence (AI) integration in nursing simulation education is growing, yet understanding its implementation across simulation phases remains limited. PURPOSE: To map AI applications across prebriefing, simulation, and debriefing phases in nursing simulation education. METHODS: Following Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines, we searched PubMed, CINAHL Complete, EMBASE, Scopus, and Web of Science (2015-2024) using terms related to nursing students, simulation, and artificial intelligence Studies were included if they involved prelicensure nursing students, AI-integrated nursing simulation education, and were peer-reviewed English publications. Data were charted using the population, concept, context framework. RESULTS: Analysis of 14 articles revealed AI applications in prebriefing (chatbots; n = 2), simulation (virtual environments; n = 11), and debriefing (feedback; n = 1). Benefits included standardization and personalized learning, while challenges involved technical limitations and faculty readiness. CONCLUSIONS: AI shows potential in enhancing nursing simulation education through standardized learning experiences but requires structured faculty support and evaluation methods.