Nicholas Wee Siong Neo MSc, RN , Joko Gunawan PhD, RN , Tracy Levett-Jones MEd, PhD, RN , Eng Tat Khoo PhD , Wei Ling Chua PhD, RN , Sok Ying Liaw PhD, RN
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OpenAI’s GPT models were most frequently used to portray simulated characters and deliver automated feedback. GenAI-enhanced simulation was generally perceived as accurate, realistic and feasible, with some evidence supporting its use as a supplement to conventional simulation and to enhance learning outcomes. Perspectives, ethical considerations and recommendations for GenAI-enhanced simulation were also highlighted.</div></div><div><h3>Conclusion</h3><div>GenAI-enhanced simulation is gaining popularity and is likely to evolve alongside human-facilitated simulation. Future developments should focus on building AI expertise among simulation educators and harnessing the synergy between human intelligence and GenAI. Further rigorous research is needed to establish best practices.</div></div>","PeriodicalId":48753,"journal":{"name":"Clinical Simulation in Nursing","volume":"108 ","pages":"Article 101819"},"PeriodicalIF":2.5000,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generative artificial intelligence in healthcare simulation-based education: A scoping review\",\"authors\":\"Nicholas Wee Siong Neo MSc, RN , Joko Gunawan PhD, RN , Tracy Levett-Jones MEd, PhD, RN , Eng Tat Khoo PhD , Wei Ling Chua PhD, RN , Sok Ying Liaw PhD, RN\",\"doi\":\"10.1016/j.ecns.2025.101819\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Aims</h3><div>This review aimed to explore the current state of generative artificial intelligence (GenAI) use in simulation-based healthcare education through a comprehensive examination of GenAI types, applications and reported outcomes.</div></div><div><h3>Methods</h3><div>A scoping review was conducted utilizing the Joanna Briggs Institute’s methodological guidance. Six databases were searched from their inception until February 2025.</div></div><div><h3>Results</h3><div>We included 28 articles that were published between 2023 and 2025. Articles were mainly in the fields of medicine (<em>n</em> = 14) and nursing (<em>n</em> = 10). OpenAI’s GPT models were most frequently used to portray simulated characters and deliver automated feedback. GenAI-enhanced simulation was generally perceived as accurate, realistic and feasible, with some evidence supporting its use as a supplement to conventional simulation and to enhance learning outcomes. Perspectives, ethical considerations and recommendations for GenAI-enhanced simulation were also highlighted.</div></div><div><h3>Conclusion</h3><div>GenAI-enhanced simulation is gaining popularity and is likely to evolve alongside human-facilitated simulation. Future developments should focus on building AI expertise among simulation educators and harnessing the synergy between human intelligence and GenAI. Further rigorous research is needed to establish best practices.</div></div>\",\"PeriodicalId\":48753,\"journal\":{\"name\":\"Clinical Simulation in Nursing\",\"volume\":\"108 \",\"pages\":\"Article 101819\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Simulation in Nursing\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1876139925001355\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Simulation in Nursing","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1876139925001355","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
Generative artificial intelligence in healthcare simulation-based education: A scoping review
Aims
This review aimed to explore the current state of generative artificial intelligence (GenAI) use in simulation-based healthcare education through a comprehensive examination of GenAI types, applications and reported outcomes.
Methods
A scoping review was conducted utilizing the Joanna Briggs Institute’s methodological guidance. Six databases were searched from their inception until February 2025.
Results
We included 28 articles that were published between 2023 and 2025. Articles were mainly in the fields of medicine (n = 14) and nursing (n = 10). OpenAI’s GPT models were most frequently used to portray simulated characters and deliver automated feedback. GenAI-enhanced simulation was generally perceived as accurate, realistic and feasible, with some evidence supporting its use as a supplement to conventional simulation and to enhance learning outcomes. Perspectives, ethical considerations and recommendations for GenAI-enhanced simulation were also highlighted.
Conclusion
GenAI-enhanced simulation is gaining popularity and is likely to evolve alongside human-facilitated simulation. Future developments should focus on building AI expertise among simulation educators and harnessing the synergy between human intelligence and GenAI. Further rigorous research is needed to establish best practices.
期刊介绍:
Clinical Simulation in Nursing is an international, peer reviewed journal published online monthly. Clinical Simulation in Nursing is the official journal of the International Nursing Association for Clinical Simulation & Learning (INACSL) and reflects its mission to advance the science of healthcare simulation.
We will review and accept articles from other health provider disciplines, if they are determined to be of interest to our readership. The journal accepts manuscripts meeting one or more of the following criteria:
Research articles and literature reviews (e.g. systematic, scoping, umbrella, integrative, etc.) about simulation
Innovative teaching/learning strategies using simulation
Articles updating guidelines, regulations, and legislative policies that impact simulation
Leadership for simulation
Simulation operations
Clinical and academic uses of simulation.