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Effectiveness of artificial intelligence-driven simulation in dental education: a systematic review and meta-analysis
Amol Ramchandra Gadbail1, Shailesh M Gondivkar2, Monal B Yuwanati3
1Department of Dentistry, Government Medical College & Hospital, Nagpur, Maharashtra, India. gadbail@yahoo.co.in.
Background:
This systematic review and meta-analysis aimed to evaluate and quantitatively synthesize the effectiveness of artificial intelligence (AI)-driven simulation (ADS) technologies in improving learning outcomes in dental education.
Methods:
The PubMed, Scopus, and Web of Science electronic databases were searched up to March 2026. Studies evaluating ADS modalities, with or without a comparator, were included. Comparative studies with appropriate control groups contributed to the meta-analysis, while all studies were included in the narrative synthesis. For multi-arm studies, each eligible intervention control pair was extracted as a separate comparison, with shared control groups appropriately adjusted to avoid double-counting. The outcomes were grouped according to Kirkpatrick's model. A random-effects meta-analysis using standardized mean differences (SMD) (Hedges' g) method was carried out.
Results:
The review included twelve studies with over 1,400 participants; five studies (including one multi-arm study yielding six independent comparisons; n = 557) contributed to the meta-analysis. ADS, considered as a heterogeneous group of AI-based educational interventions, was associated with improved overall learning outcomes compared to traditional methods (SMD = 1.20; 95% CI: 0.73-1.67). However, moderate-to-high heterogeneity was observed, and findings should be interpreted cautiously. Effects appeared more pronounced in immersive and feedback-driven modalities, although these observations are exploratory and based on limited evidence. Improvements were reported across multiple domains, reflecting a general (composite) educational effect across diverse interventions and outcome measures. Overall, while ADS shows potential benefit, the certainty of evidence remains limited.
Conclusion:
ADS, as a heterogeneous group of AI-based educational interventions, shows potential to improve overall learning outcomes in dental education, particularly in psychomotor and cognitive domains. However, these findings represent a composite educational effect across diverse modalities and should be interpreted cautiously due to heterogeneity, a limited number of studies, methodological variability, and predominantly short-term outcomes.