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The effectiveness of GAI-assisted teaching methods in medical education: a systematic review and meta-analysis
Xingming Ma1, Xianting Liu2, Haojie Sun2
1School of Social Work and Health Management, Xihua University, Chengdu, China.
Objective:
The study evaluated the effectiveness of generative artificial intelligence (GAI)-assisted teaching methods on the medical educational outcomes.
Methods:
Following 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 I 2 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.
Results:
A 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).
Conclusions:
The 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.
