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Integration of Artificial Intelligence in Nursing Simulation Education: A Scoping Review
Maggie Mee Kie Chan1, Abraham Wai Him Wan, Daphne Sze Ki Cheung
1Author Affiliations: School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China (Drs Maggie Mee Kie Chan, Engle Angela Chan, and Yorke); School of Nursing, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Mr Wan, Dr Choi, and Ms Wang); Centre for Quality and Patient Safety Research/ Alfred Health Partnership, Institute for Health Transformation, Deakin University, Melbourne, Australia (Dr Cheung); and School of Nursing and Midwifery, Faculty of Health, Deakin University, Burwood, Melbourne, VIC, Australia (Dr Cheung).
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.
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