AI-Enabled automated feedback and debriefing in simulation-based healthcare education: a systematic review of educational effectiveness
DOI:
https://doi.org/10.65759/jwrsv661Keywords:
Artificial intelligence, automated feedback, debriefing, simulation based education, healthcare educationAbstract
Artificial intelligence is integrated into healthcare simulation to deliver individualized feedback and support reflective learning. Educational evaluations include procedural coaching, virtual patient encounters, and facilitator development. This review examined educational effectiveness while distinguishing observed performance from perceived competence and feedback feasibility. Original research published to May 2026 evaluating artificial intelligence enabled feedback or debriefing in healthcare simulation was reviewed. Comparative educational studies and complementary feedback validation or feasibility investigations were synthesized narratively. Outcomes included technical performance, clinical reasoning, communication, perceived competence, cognitive workload, and debriefing utility. Ten original reports comprised seven randomized trials and three prospective, pilot, or qualitative investigations. Surgical trials show improvements in selected performance measures, with benefits dependent on feedback timing, instructional design, and human participation. Personalized expert instruction informed by artificial intelligence exceeded artificial intelligence tutoring alone in practice and transfer tasks. Virtual patient feedback improved clinical reasoning in a small trial, while nursing simulations demonstrated gains in communication and perceived competence. Artificial intelligence enabled feedback offers benefits for simulation outcomes. Data for autonomous team debriefing is less developed than data for individualized coaching. Integration with educators, independent assessment, and longitudinal evaluation provides the strongest direction for educational implementation.
References
1. Cook DA, Hatala R, Brydges R, Zendejas B, Szostek JH, Wang AT, et al. Technology-enhanced simulation for health professions education: a systematic review and meta-analysis. JAMA. 2011;306(9):978–988.
2. Issenberg SB, McGaghie WC, Petrusa ER, Gordon DL, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2005;27(1):10–28.
3. McGaghie WC, Issenberg SB, Cohen ER, Barsuk JH, Wayne DB. Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence. Acad Med. 2011;86(6):706–711.
4. Fanning RM, Gaba DM. The role of debriefing in simulation-based learning. Simul Healthc. 2007;2(2):115–125.
5. Cheng A, Eppich W, Grant V, Sherbino J, Zendejas B, Cook DA. Debriefing for technology-enhanced simulation: a systematic review and meta-analysis. Med Educ. 2014;48(7):657–666.
6. Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, Del Maestro RF. The Virtual Operative Assistant: an explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS One. 2020;15(2):e0229596.
7. Yilmaz R, Winkler-Schwartz A, Mirchi N, Reich A, Christie S, Tran DH, et al. Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation. NPJ Digit Med. 2022;5:54.
8. Fazlollahi AM, Bakhaidar M, Alsayegh A, Yilmaz R, Winkler-Schwartz A, Mirchi N, et al. Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students: a randomized clinical trial. JAMA Netw Open. 2022;5(2):e2149008.
9. Yilmaz R, Bakhaidar M, Alsayegh A, Abou Hamdan N, Fazlollahi AM, Tee T, et al. Real-Time multifaceted artificial intelligence vs In-Person instruction in teaching surgical technical skills: a randomized controlled trial. Sci Rep. 2024;14:15130.
10. Giglio B, Albeloushi A, Alhaj AK, Alhantoobi M, Saeedi R, Davidovic V, et al. Artificial intelligence–augmented human instruction and surgical simulation performance: a randomized clinical trial. JAMA Surg. 2025;160(9):984–991.
11. Yilmaz R, Alsayegh A, Bakhaidar M, Fazlollahi AM, Abou Hamdan N, Tee T, et al. Combining real-time AI and in-person expert instruction in simulated surgical skills training—randomized crossover trial. NPJ Artif Intell. 2025;1:36.
12. Brügge E, Ricchizzi S, Arenbeck M, Keller MN, Schur L, Stummer W, et al. Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial. BMC Med Educ. 2024;24:1391.
13. Holderried F, Stegemann-Philipps C, Herrmann-Werner A, Festl-Wietek T, Holderried M, Eickhoff C, et al. A language model–powered simulated patient with automated feedback for history taking: prospective study. JMIR Med Educ. 2024;10:e59213.
14. Fung TCJ, Chan SL, Lam CFM, Lam CY, Cheng CCW, Lai MH, et al. Effects of generative artificial intelligence (GenAI) patient simulation on perceived clinical competency among global nursing undergraduates: a cross-over randomised controlled trial. BMC Nurs. 2025;24:934.
15. Dai X, Gong Y, Ma LY. Effects of ChatGPT-generated immediate feedback integrated into VR-based OSCEs on nursing students’ performance: a randomized crossover study. BMC Nurs. 2026;25:396.
16. Vaughn J, Ford SH, Sheckells A, Fasnacht H, Crawford D, Dogan G. Enhancing simulation facilitator debriefing using a generative artificial intelligence feedback interface and retrieval-augmented generation: a pilot study. Clin Simul Nurs. 2025;109:101843.
17. Tscholl DW, Ebensperger M, et al. Generative AI in simulation debriefings: an exploratory study using the Team-FIRST framework and qualitative feedback from simulation experts and learners. Adv Simul (Lond). 2026;11:14.
18. Fazlollahi AM, Yilmaz R, Winkler-Schwartz A, Mirchi N, Ledwos N, Bakhaidar M, et al. AI in surgical curriculum design and unintended outcomes for technical competencies in simulation training. JAMA Netw Open. 2023;6(9):e2334658.
19. Eppich W, Cheng A. Promoting Excellence and Reflective Learning in Simulation (PEARLS): development and rationale for a blended approach to health care simulation debriefing. Simul Healthc. 2015;10(2):106–115.
20. Rudolph JW, Simon R, Dufresne RL, Raemer DB. There’s no such thing as “nonjudgmental” debriefing: a theory and method for debriefing with good judgment. Simul Healthc. 2006;1(1):49–55.
21. Kolbe M, Weiss M, Grote G, Knauth A, Dambach M, Spahn DR, et al. TeamGAINS: a tool for structured debriefings for simulation-based team trainings. BMJ Qual Saf. 2013;22(7):541–553.
Neo NWS, Gunawan J, Levett-Jones T, Khoo ET, Chua WL, Liaw SY. Generative artificial intelligence in healthcare simulation-based education: a scoping review. Clin Simul Nurs. 2025;108:101819.
