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

AI-Powered Simulation for Nursing Education: Mixed Methods Systematic Review — A PRISMA-guided mixed-methods systematic review by Jiang et al. (2026) synthesizing 19 studies (N = 1,253, mostly prelicensure nursing students) on AI-powered simulations in nursing education. Evidence from the three RCTs and controlled quasi-experimental studies shows significant gains in cognitive knowledge and affective outcomes (Self Efficacy, communication confidence), but inconsistent effects on complex psychomotor skills — one RCT even found AI-assisted simulation inferior to standardized-patient simulation. Qualitative meta-aggregation reveals learners value safe, repeatable, nonjudgmental practice that bridges the theory–practice gap, while persistent "authenticity gap" frustrations (robotic dialogue, missing nonverbal cues, technical instability) mean AI should be a complement to — not a replacement for — traditional simulation and clinical placement.

Key Findings

• Positive but conditional evidence. AI-powered simulation significantly improved cognitive knowledge and affective outcomes (self-efficacy, communication confidence) in the strongest designs (3 RCTs + controlled quasi-experiments). Effects on complex psychomotor skills were inconsistent, and one RCT found AI-assisted simulation inferior to standardized-patient simulation for these.
• Four AI modality categories. The included studies spanned (1) generative AI/LLMs (n=7, e.g., ChatGPT-driven virtual patients), (2) AI-driven virtual patients/mannequins (n=5), (3) AI-enhanced virtual/mixed reality (n=5), and (4) chatbots (n=2) — a heterogeneous field that precluded quantitative meta-analysis.
• Learners value safe, repeatable practice. Qualitative meta-aggregation found students appreciated low-risk environments where they could attempt tasks repeatedly without judgment — building confidence and bridging the theory–practice gap — and valued consistent, repeatable AI interactions for foundational skills.
• The "authenticity gap." Learners consistently perceived AI interactions as lacking emotional depth, nonverbal cues, and tactile/physical-examination dimensions. Technical failures (e.g., speech-recognition delays) disrupted interaction flow and could raise extraneous cognitive load and state anxiety. This authenticity gap is why AI is best for highly structured objectives (foundational communication, history-taking, health education) and poorly suited to emotionally or interpersonally complex scenarios.
• Learner acceptance was moderate-to-high. Usability, acceptability, and perceived usefulness were consistently rated moderate to high across included studies (SUS and TAM measures), with learners recognizing value for Accessibility and structured guidance — but acceptance of AI feedback is conditioned by trust: learners prefer human feedback sources perceived as having "benevolence" and "integrity" over AI perceived as merely "competent," especially for emotion-intensive tasks.
• A stepped simulation continuum is recommended. Rather than wholesale replacement, educators should stage AI across the curriculum — AI for pre-learning, history-taking, and foundational reasoning early; high-fidelity human simulators and standardized patients for complex psychomotor skills and emotional intelligence later. Hybrid interaction modes (menu-based for novices, open-ended/voice GenAI for advanced learners) and human-facilitated debriefing alongside AI feedback are key implementation levers.
• Evidence base is limited. The review is dominated by uncontrolled designs, relies heavily on self-report, and has no longitudinal data on skill retention or clinical transfer; the near-uniform positive result pattern raises publication-bias concerns; and no studies came from Africa, South America, or low-income countries (with a Greater China concentration).

Connected Concepts

• Medical Education
• Simulation
• Generative AI
• LLM
• Self Efficacy
• Student Experience
• Meta Analysis Systematic Review

Connected Articles

• Alrazeeni Transforming Nursing Education AI 2026 — Transforming nursing education with AI: systematic review (2010–2025)
• GenAI Scenario Based Healthcare Education 2026 — Systematic review of GenAI in scenario-based healthcare education
• Medgame LLM Medical Education Gamification — Gamified LLM-based learning for medical education
• Adaptive Virtual Patient Psychotherapy Training — Adaptive virtual patient for psychotherapy training
• GenAI Simulate Patient History Pbl 2026 — GenAI to simulate patient histories in problem-based learning
• AI Teammate Task Distribution Medical Training 2026 — SCAN framework for AI task distribution in medical training

Citation

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, 28, e95167.