TY - JOUR
T1 - Integration of Artificial Intelligence in Nursing Simulation Education
T2 - A Scoping Review
AU - Chan, Maggie Mee Kie
AU - Wan, Abraham Wai Him
AU - Cheung, Daphne Sze Ki
AU - Choi, Edmond Pui Hang
AU - Chan, Engle Angela
AU - Yorke, Janelle
AU - Wang, Lizhen
N1 - Publisher Copyright:
© 2025 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - clinical competency
KW - digital technology
KW - educational innovation
KW - machine learning
KW - virtual learning environment
UR - https://www.scopus.com/pages/publications/105003191853
U2 - 10.1097/NNE.0000000000001851
DO - 10.1097/NNE.0000000000001851
M3 - Journal article
AN - SCOPUS:105003191853
SN - 0363-3624
JO - Nurse Educator
JF - Nurse Educator
M1 - 10.1097/NNE.0000000000001851
ER -