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Background <p>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.</p> Methods <p>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.</p> Results <p>The review included twelve studies with over 1,400 participants; five studies (including one multi-arm study yielding six independent comparisons; <i>n</i> = 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.</p> Conclusion <p>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.</p>

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Effectiveness of artificial intelligence-driven simulation in dental education: a systematic review and meta-analysis of learning outcomes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Amol Ramchandra Gadbail,
  • Shailesh M. Gondivkar,
  • Monal B Yuwanati,
  • Archana Sonone,
  • Mithilesh Dhamande,
  • Aarti Panchbhai,
  • Alka H Hande,
  • Swati Patil,
  • Sachin C. Sarode

摘要

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.