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Systematic review and meta-analysis of GAI-assisted teaching methods in medical students shows improved academic performance.

Systematic review and meta-analysis of GAI-assisted teaching methods in medical students shows impro…
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Key Takeaway
Consider GAI-assisted teaching methods for medical students to improve academic performance and learning outcomes based on this meta-analysis.

This systematic review and meta-analysis assessed the impact of GAI-assisted teaching methods within medical education settings, including clinical, nursing, and dentistry medicine. The analysis included data from 3,635 medical students in the GAI-assisted teaching group and 3,931 medical students in the control group.

Primary outcomes focused on academic performance. Knowledge scores were significantly improved with SMD = 0.95 (95% CI: 0.72–1.18, p < 0.05). Practical scores also showed significant improvement with SMD = 1.48 (95% CI: 1.20–1.77, p < 0.05).

Secondary outcomes also improved, including student satisfaction (SMD = 1.52, 95% CI: 1.01–2.02, p < 0.05), self-efficacy in learning (SMD = 0.75, 95% CI: 0.17–1.32, p < 0.05), and learning initiative (SMD = 1.20, 95% CI: 0.10–2.30, p < 0.05). Self-directed learning ability improved with SMD = 1.25 (95% CI: 0.81–1.69, p < 0.05), clinical thinking ability with SMD = 1.18 (95% CI: 0.86–1.50, p < 0.05), and analytical and problem-solving skills with SMD = 1.53 (95% CI: 0.77–2.29, p < 0.05).

Safety data were not reported for adverse events, serious adverse events, discontinuations, or tolerability. The authors note no specific limitations in the provided text. Practice relevance suggests policymakers should consider integrating artificial intelligence into teacher training and medical curriculum design to improve learning outcomes. These findings highlight potential educational applications.

Study Details

Study typeMeta analysis
EvidenceLevel 1
PublishedApr 2026
View Original Abstract ↓
ObjectiveThe study evaluated the effectiveness of generative artificial intelligence (GAI)-assisted teaching methods on the medical educational outcomes.MethodsFollowing the PRISMA guidelines, a systematic search was conducted for literature on AI-assisted educational interventions in medical education (e.g. clinical, nursing and dentistry medicine). PROSPERO registration number was CRD420251173150. Meta-analyses of the outcomes were performed using the Review Manager 5.4. Heterogeneity was evaluated using the I2 statistic and Cochran's Q test. A forest plot, Egger's test and the trim-and-fill method were used to evaluate publication bias and robustness.ResultsA total of 5,764 publications was initially retrieved, of which 78 studies involving 3,635 medical students in the GAI-assisted teaching group and 3,931 medical students in the control group were included. The pooled results revealed that GAI-assisted teaching significantly improved academic performance in terms of both knowledge (SMD = 0.95, 95% CI: 0.72–1.18, p < 0.05) and practical (SMD = 1.48, 95% CI: 1.20–1.77, p < 0.05) scores, compared to the control group. Additional benefits included improved student satisfaction (SMD = 1.52, 95% CI: 1.01–2.02, p < 0.05), self-efficacy in learning (SMD = 0.75, 95% CI: 0.17–1.32, p < 0.05), learning initiative (SMD = 1.20, 95% CI: 0.10–2.30, p < 0.05), self-directed learning ability (SMD = 1.25, 95% CI: 0.81–1.69, p < 0.05), clinical thinking ability (SMD = 1.18, 95% CI: 0.86–1.50, p < 0.05) and analytical and problem-solving skills (SMD = 1.53, 95% CI: 0.77–2.29, p < 0.05).ConclusionsThe results showed that the GAI-assisted teaching could improve efficiently various aspects of education outcomes for medical students, including academic performance, self-efficacy and initiative in learning, and skills development. In future, policymakers should consider integrating artificial intelligence into teacher training and medical curriculum design to improve learning outcomes.
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