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AI-Powered Simulation for Nursing Education: Mixed Methods Systematic Review
Hongzhan Jiang1, Ziyan Wang1, Wanting Shen1
1School of Nursing, Beijing University of Chinese Medicine, Liangxiang University Town, Fangshan District, Beijing, 100000, China, 86 18911091028.
Journal of Medical Internet Research
|July 21, 2026
Summary
AI simulations enhance nursing education by improving knowledge and confidence, but face challenges in replicating real-world authenticity. They should complement, not replace, traditional methods for optimal skill development.
Area of Science:
- Nursing Education
- Artificial Intelligence
- Simulation Technology
Background:
- Traditional nursing simulation is costly and limited in scalability.
- Artificial intelligence (AI)-powered simulations offer scalable, personalized learning alternatives.
- Evidence on AI simulation effectiveness and acceptance in nursing is fragmented.
Purpose of the Study:
- To systematically evaluate and synthesize evidence on AI-powered simulation effectiveness in nursing education.
- To assess learner perceptions and acceptance of AI simulations in nursing training.
Main Methods:
- Systematic literature search across 11 databases (Jan 2014-Sep 2025) following PRISMA guidelines.
- Independent reviewer selection, data extraction, and quality appraisal using multiple bias tools (RoB 2, ROBINS-I, MMAT, JBI, AHRQ).
- Narrative synthesis of quantitative data and meta-aggregation of qualitative findings using a convergent segregated approach.
Main Results:
- Nineteen studies (1253 participants) utilized various AI modalities (generative AI, virtual patients, VR/MR, chatbots).
- AI simulations significantly improved cognitive knowledge and affective outcomes (self-efficacy, confidence) in controlled studies.
- Qualitative data highlighted value in safe practice environments but noted challenges like technical issues and lack of authenticity, creating an "authenticity gap".
Conclusions:
- AI simulations show promise for foundational clinical reasoning and communication skills in nursing.
- Current evidence is limited by study design and reliance on self-reported measures.
- AI should complement traditional methods; future research needs longitudinal data and standardized measures.
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