Currently submitted to: Journal of Medical Internet Research
Date Submitted: May 18, 2026
Open Peer Review Period: May 19, 2026 - Jul 14, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Artificial Intelligence in Simulation-based Healthcare Education: A Systematic Literature Review
ABSTRACT
Background:
Simulation-based learning (SBL) is widely used in healthcare education to develop both clinical competence and essential soft skills such as communication and teamwork. Although digital and simulation technologies have expanded rapidly, the specific role of artificial intelligence (AI) in enhancing and transforming these environments remains fragmented and insufficiently understood. Current knowledge is limited by small-scale implementations, narrow disciplinary focus, and weak connections between technological innovation and educational theory, underscoring the need for a comprehensive synthesis of evidence.
Objective:
This study aimed to systematically review and map the landscape of AI-supported SBL in healthcare education, identifying trends, approaches, theoretical foundations, and ethical considerations.
Methods:
We conducted a systematic literature review of published studies on AI-supported SBL across healthcare contexts. Articles were analysed for geographic and disciplinary distribution, learner populations, simulation technologies, AI techniques, educational frameworks, and reporting of ethical practices. A structured approach was used to compare technological, pedagogical, and methodological characteristics across studies.
Results:
AI-supported SBL research is concentrated in developed countries and primarily targets undergraduate medical and nursing education, with limited attention to other professions, interprofessional learning, or continuing development. Most initiatives are small-scale and experimental, relying heavily on computer-based platforms and large language model-driven conversational agents, while immersive and multimodal simulations remain underexplored. Weak theoretical grounding, limited attention to learner experience, and insufficient engagement with ethical issues such as bias and data governance persist.
Conclusions:
Further investigation of theory-informed, ethically robust AI-supported SBL across diverse healthcare settings may strengthen educational effectiveness and broader competency development.
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