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