Learners’ experiences of generative artificial intelligence-supported simulation and skills training in nursing education: a meta-synthesis
Generative artificial intelligence (GenAI) is increasingly being used in nursing simulation and skills training. Implementation and scaling decisions require learner-centered evidence on educational value, limitations, and safety. To synthesize nursing learners’ experiences of GenAI-supported simulation and skills training and explore perceived educational value and safeguards for learning. We conducted a meta-synthesis of studies identified through MEDLINE, Embase, CINAHL, PsycINFO, ERIC, and citation tracking (November 2022–June 21, 2026). Eligibility criteria were structured using the SPIDER framework. Two reviewers independently screened records, extracted data, and appraised methodological quality using the JBI Critical Appraisal Checklist for Qualitative Research. Findings were synthesized using Thomas and Harden’s thematic synthesis, and confidence was assessed using GRADE-CERQual. Fifteen reports representing 14 studies were included. Four analytical themes were developed: (1) accessible rehearsal and perceived gains in confidence and readiness (moderate confidence); (2) AI-mediated interaction and perceived support for communication, reasoning, and case application (high confidence); (3) perceived realism and persistent relational and technical limits (moderate confidence); and (4) verification, feedback, and reflection as perceived safeguards (moderate confidence). The perceived educational value of these activities appeared to be shaped by low-pressure practice, responsive interaction, and opportunities to evaluate generated content. Relational limitations, technical disruption, and unreliable outputs constrained these experiences. Short exposures and uneven qualitative depth limit conclusions about sustained learning. Learner experiences support considering GenAI as a supervised rehearsal partner alongside established simulation modalities. Combined with simulation guidance, the findings highlight the value of verification, feedback, and debriefing. Brief exposures and uneven qualitative depth warrant longer-term research on sustained learning, clinical transfer, and safety.
Authors
- 杨力铭
- Yanling Hu (ORCID: https://orcid.org/0009-0004-8512-7138)
- Tang Mengyan
- Yuan Li
- Shuying Cao
- Yinyue Zhang
- Qiong Chen
Institutions
- Sichuan University (CN)
- West China Medical Center of Sichuan University (CN)
- West China Second University Hospital of Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
Publication Details
- Journal
- BMC Medical Education
- Published
- 2026-11-05
- DOI
- https://doi.org/10.1186/s12909-026-10792-9
- Primary Topic
- Simulation-Based Education in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00