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AI-based teaching improves knowledge, skills, and satisfaction scores among medical students compared to traditional methodsAI tools improve knowledge and skills for medical students

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Key Takeaway
Note that AI-based teaching significantly improves knowledge, skills, and satisfaction scores in medical students.

This meta-analysis of randomized controlled trials evaluated the impact of artificial intelligence (AI)-based teaching compared to traditional-based teaching among a sample of 1116 medical students. The analysis focused on knowledge scores, skills scores, and teaching satisfaction scores as primary and secondary outcomes.

The meta-analysis reported statistically significant improvements across all measured outcomes for the AI-based teaching group. Knowledge scores showed a significant increase (SMD 0.36; 95% CI, 0.24-0.49; P <.00001). Skills scores were significantly higher in the AI-based group (SMD 0.78; 95% CI, 0.57-0.99; P <.00001). Additionally, teaching satisfaction scores were significantly higher in the AI-based group (SMD 0.97; 95% CI, 0.66-1.29; P <.00001).

The authors noted several limitations, including high between-study heterogeneity and potential publication or reporting bias. While the results suggest potential benefits for medical education, the authors note that further research is needed to evaluate AI-based teaching in postgraduate and subspecialty settings. The current evidence does not confirm efficacy in those specific postgraduate contexts.

Learning to be a doctor is a demanding journey that requires mastering complex facts and hands-on skills. A large review of studies involving over 1,100 medical students looked at what happens when artificial intelligence is added to the classroom. The results suggest that students using AI-based teaching performed better than those in traditional settings.

Specifically, the data showed that students using AI had significantly higher scores in both general knowledge and practical skills. These students also reported much higher levels of satisfaction with their teaching. While the results are promising, the researchers noted that the data comes from a meta-analysis, which can sometimes be affected by how individual studies were reported or published.

Because the study focused on students in medical education, the findings are not yet proven for doctors in specialized postgraduate training. However, the clear jump in skill scores and student satisfaction suggests that AI could be a powerful tool for those just starting their medical careers.

What this means for you:
Medical students using AI-based teaching showed higher knowledge, better skills, and more satisfaction.

Common questions

How does AI-based teaching affect medical students?

Students who used AI-based teaching scored significantly higher in both knowledge and skills compared to those in traditional settings. They also reported much higher satisfaction with their teaching. These results were based on a review of data from 1,116 students.

Is AI-based teaching better than traditional methods?

The data shows that AI-based teaching led to significantly higher knowledge scores and skills scores. While the study does not prove it is better for every specific situation, the results show a clear improvement over traditional methods for the students involved.

Study Details

Study typeMeta analysis
EvidenceLevel 1
PublishedSep 2026
View Original Abstract ↓
AIMS: To evaluate comparative outcomes of artificial Intelligence (AI)-based and traditional-based teaching in medical education. METHODS: The literature search was carried out in CENTRAL, CINAHL, Web of Science, MEDLINE, and EMBASE to identify randomized controlled trials (RCTs) comparing AI-based versus traditional-based teachings in medical education. Estimate of effect size was determined for knowledge score, skills score, and teaching satisfaction score via fixed-effect modelling. RESULTS: Fourteen RCTs enrolling 1116 students who received AI-based teaching (n = 558) or traditional-based teaching (n = 558) were included. The use of AI-based teaching was associated with significantly higher knowledge score (standardized mean difference (SMD): 0.36, 95% CI, 0.24-0.49, P < .00001), skills score (SMD: 0.78, 95% CI, 0.57-0.99, P < .00001), and teaching satisfaction score (SMD: 0.97, 95% CI, 0.66-1.29, P < .00001) compared to the traditional-based teaching. Subgroup analyses with respect to the practical course, theoretical course, duration of course shorter or longer than 1 week were consistent with the main analyses. Meta-regression analysis demonstrated that practical course significantly increased estimate effect for knowledge score (P = .002) and skills score (P = .0001). CONCLUSIONS: Meta-analysis of best available evidence (level 1a) indicates that AI-based teaching significantly improves student's knowledge, skills, and satisfactions compared to traditional teaching. However, the available evidence may be subject to publication and reporting bias with high between-study heterogeneity. Future studies should evaluate AI-based teaching in postgraduate settings including speciality and even subspecialties trainings. Key messages What is already known on this topic: Growing evidence from randomized controlled trials demonstrated positive impact of artificial intelligence (AI) in medical education when compared to the traditional approaches. What this study adds: Meta-analysis of best available evidence (level 1a) indicates that AI-based teaching significantly improves student's knowledge, skills, and satisfactions compared to traditional teaching. How this study might affect research, practice, or policy: This study suggests that Future studies should evaluate AI-based teaching in postgraduate settings including speciality and even subspecialties trainings.
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