Applications of Generative Artificial Intelligence Teaching in Nursing Education: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: Generative artificial intelligence (GenAI) is increasingly used in nursing education. Its educational effects in nursing remain uncertain. Purpose: To evaluate the effects of using GenAI in teaching nursing. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were searched from inception to July 2026. Standardized mean differences and 95% confidence intervals (95% CI) were pooled using random-effects models. Heterogeneity, sensitivity analyses, meta-regression, and small-study effects were explored. Results: Seventeen studies involving 1456 participants were included. GenAI-integrated teaching was associated with higher knowledge scores (standardized mean differences 0.53, 95% CI 0.03-1.04), practice-related scores (SMD 1.25, 95% CI 0.23-2.28), and teaching satisfaction (SMD 0.58, 95% CI 0.02-1.13). Attitudes, competency, and self-efficacy did not differ significantly. Meta-regression findings were exploratory. Conclusions: GenAI-integrated teaching may improve knowledge, practice-related performance, and satisfaction in nursing education. However, extreme heterogeneity and sensitivity to individual studies limit confidence in the magnitude and reproducibility of these effects. GenAI should be used as a supervised pedagogical adjunct pending high-quality trials.