SynthesisBMC medical education2026
Effectiveness of artificial intelligence-driven simulation in dental education: a systematic review and meta-analysis of learning outcomes.
Synthesis in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
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Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis 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.
methodsThe 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.
resultsThe 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.
conclusionADS, 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.