Generative artificial intelligence in healthcare simulation-based education: A scoping review
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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.
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Existing scholarly literature on GenAI's application in healthcare often focuses almost exclusively on technical performance indicators such as accuracy rates, time efficiency, and comparative error reduction against human benchmarks.
“Existing scholarly literature17–22 on GenAI’s application in healthcare often focuses almost exclusively on technical performance indicators such as accuracy rates, time efficiency, and comparative error reduction against human benchmarks.”
From Generative Artificial Intelligence in Healthcare: Automation Bias, Deskilling, and Cognitive Implications – A Systematic Review (2026) · cites this paper as [19]
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- Authors
- Neo, N. W. S., Gunawan, J., Levett-Jones, T., Khoo, E. T., Chua, W. L., & Liaw, S. Y.
- Affiliations
- National University of Singapore (SG)
- University of Technology Sydney (AU)
- Corresponding author
- Neo, Nicholas Wee Siong
- ORCID
- Venue
- Clinical Simulation in Nursing
- Publisher
- Elsevier BV
- Published
- 2025-11
- Volume
- 108
- Pages
- 101819
- ISSN
- 1876-1399 (ISSN-L)
- OpenAlex
- W4414804561
- Cited by
- 15
- References
- 52
- Citations by year
- 2026: 14 · 2025: 1
- FWCI
- 9.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Clinical Simulation in Nursing (publishedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Keywords
- generative AI, simulation-based education, healthcare education, GPT models, standardized patients, automated feedback, medical education, nursing education, AI ethics, learning outcomes
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