AI-Powered Simulation for Nursing Education: Mixed Methods Systematic Review

Clinical Snapshot

Key Findings

• P Value: Individual study p-values not extractable from the abstract; described as 'significant' for cognitive and affective outcomes in controlled designs.

• Effect Size: Not quantified; narrative synthesis only. No pooled effect sizes reported across any outcome domain.

• Primary Outcome: AI-powered simulation demonstrated significant improvements in cognitive knowledge acquisition and affective outcomes (self-efficacy, communication confidence) in RCTs and controlled quasi-experimental studies. Effects on complex psychomotor skills were inconsistent, with one RCT finding AI-assisted simulation inferior to standardised patient simulation for skill performance. Qualitative meta-aggregation identified a valued 'safe, repeatable, nonjudgmental practice environment' alongside a persistent 'authenticity gap' characterised by technical frustrations, robotic interactions, and absence of nonverbal cues.

• Nnt Or Sensitivity: Not applicable; no NNT, sensitivity, specificity, or hazard ratio calculable from narrative synthesis. Educational effect sizes (e.g., Cohen's d) from individual studies are not aggregated.

• Confidence Interval: Not reported; no meta-analytic pooling performed.

Clinical Application

Feasibility is contingent on institutional investment in AI simulation infrastructure, technical support capacity, and faculty training. The review identifies system instability and technical frustrations as persistent implementation barriers. Cost-effectiveness data are absent from the evidence base. Scalability is theoretically advantageous over traditional high-fidelity simulation but requires robust digital infrastructure. In the Australian context, AI-powered simulation is not currently embedded in NMBA (Nursing and Midwifery Board of Australia) accreditation standards or ANMAC programme requirements. RACGP and ACN have not issued formal guidance on AI simulation as a substitute for supervised clinical placement hours. TGA regulation of AI-based educational tools as software-as-a-medical-device (SaMD) may apply if clinical decision support functions are incorporated. Australian nursing schools considering adoption should treat AI simulation as supplementary to, not replacing, ANMAC-mandated clinical placement hours. The PBS is not relevant to this educational intervention. Equity considerations are pertinent given variable digital infrastructure across rural, remote, and under-resourced nursing programmes. Prelicensure undergraduate nursing students in tertiary education settings. Some applicability to interdisciplinary health professional education cohorts. Not yet validated for postgraduate, specialist, or continuing professional development contexts.

Abstract

BACKGROUND: Traditional simulation-based nursing education is often constrained by high costs, resource intensity, and limited scalability. AI-powered simulations offer dynamic, scalable, and personalized alternatives. However, the empirical evidence regarding their pedagogical effectiveness and learner acceptance remains fragmented. OBJECTIVE: This study aimed to systematically evaluate and synthesize evidence on the effectiveness and learner perceptions of AI-powered simulations in nursing education. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched 11 electronic databases (PubMed, CINAHL, Embase, Web of Science, Cochrane Library, Scopus, SinoMed, CNKI, Wanfang, VIP, and Google Scholar) for studies published between January 2014 and September 2025. Two independent reviewers performed study selection, data extraction, and quality appraisal using design-specific tools (risk of bias 2 tool [RoB 2; Cochrane Bias Methods Group] for randomized controlled trials [RCTs], Risk Of Bias in Nonrandomized Studies of Interventions [ROBINS-I; Cochrane Bias Methods Group] for nonrandomized studies, Mixed Methods Appraisal Tool [MMAT] for mixed methods, Joanna Briggs Institute [JBI] for qualitative, and Agency for Healthcare Research and Quality [AHRQ] for cross-sectional studies). Quantitative data were synthesized narratively, and qualitative findings were integrated using JBI meta-aggregation. A convergent segregated approach with joint display was used to generate meta-inferences. RESULTS: Nineteen studies involving 1253 participants (primarily prelicensure nursing students, with some interdisciplinary cohorts) were included. AI modalities comprised generative AI/large language models (n=7), AI-driven virtual patients/mannequins (n=5), AI-enhanced virtual/mixed reality (n=5), and chatbots (n=2). Three studies were RCTs, 4 were quasiexperimental with control groups, 3 were uncontrolled pre-post studies, 4 were mixed methods, 4 were qualitative, and one was a cross-sectional survey. Quantitative synthesis showed that evidence from RCTs and controlled quasiexperimental studies indicates significant improvements in cognitive knowledge and affective outcomes, including self-efficacy and communication confidence; however, effects on complex psychomotor skills were inconsistent, with one RCT finding AI-assisted simulation inferior to standardized patient simulation. Findings from uncontrolled designs are preliminary. Qualitative meta-aggregation revealed that learners valued safe, repeatable, nonjudgmental practice environments that reduced anxiety and bridged the theory-practice gap. Persistent challenges included technical frustrations, "robotic" interactions, lack of nonverbal cues, and system instability, collectively constituting an "authenticity gap." CONCLUSIONS: AI-powered simulations show promise for developing foundational clinical reasoning and communication skills in nursing education, though the evidence base is limited by the predominance of uncontrolled designs, reliance on self-reported measures, and absence of longitudinal data on skill retention or clinical transfer. Due to current technological limitations in replicating physical and emotional authenticity, AI should be implemented as a complementary tool alongside traditional simulation methods and clinical placements, rather than as a replacement. Future research should prioritize longitudinal outcomes, standardized competency measures, RCTs with active comparators, and implementation strategies addressing technical barriers.

References

1. 1.Jiang, H., Wang, Z., Shen, W., Meng, M., Yang, D., Li, X., & Hao, Y. (2026). AI-powered simulation for nursing education: Mixed methods systematic review. Journal of Medical Internet Research. https://doi.org/10.2196/95167