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TRUVACE RECORD VERSION
record: TRV-2026-2789
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:24:12.302213Z
status: published
lens: g_space
sector: education
headline: Effectiveness of artificial intelligence-driven simulation in dental education: a systematic review and meta-analysis of learning outcomes
dek: 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…
gain_title: A systematic review and meta-analysis of five comparative studies (557 dental students) found AI-driven simulation was associated with better overall learning outcomes than traditional training (SMD 1.20; 95% CI 0.73-1.67).
problem_title: (none)
trace_subject: (none)
gain_reading: A systematic review and meta-analysis of five comparative studies (557 dental students) found AI-driven simulation was associated with better overall learning outcomes than traditional training (SMD 1.20; 95% CI 0.73-1.67).
gain_evidence: 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).
problem_reading: (none)
problem_evidence: (none)
quick_read: A systematic review and meta-analysis searched PubMed, Scopus and Web of Science up to March 2026 and included twelve studies with over 1,400 dental students. Five comparative studies, yielding six comparisons and 557 participants (292 AI, 265 control), were pooled. AI-driven simulation was associated with better overall learning outcomes than traditional training (SMD = 1.20; 95% CI 0.73 to 1.67).

The effect was largest for intelligent tutoring systems (SMD 1.86) and VR-based haptic simulation (SMD 1.62), and smaller for generative AI (SMD 0.67). Mobile AI showed a positive but non-significant effect (SMD 0.88). These modality differences are exploratory and rest on few studies.

The pooled figure mixes hand-skill, knowledge and self-reported outcomes, so it is a composite effect rather than a measure of one skill. Heterogeneity was moderate to high (I² = 68.9%), and a trim-and-fill adjustment lowered the estimate to SMD 1.04. No study measured patient outcomes.
limitation: The pooled estimate combines randomized and non-randomized studies, mixes psychomotor, cognitive and subjective outcomes, shows moderate-to-high heterogeneity, and rests on few, mostly short-term studies with moderate-to-serious risk of bias.
tag: Evidence-backed gain
key_points: Twelve studies with over 1,400 participants were included; five comparative studies with 557 students were pooled in the meta-analysis. | The pooled effect favoured AI simulation over traditional training (SMD = 1.20; 95% CI 0.73 to 1.67; p = 0.0013). | Intelligent tutoring systems showed the largest effect (SMD 1.86), followed by VR haptic simulation (SMD 1.62); generative AI showed SMD 0.67. | Excluding studies with imputed standard deviations lowered the effect to SMD 0.94 (95% CI 0.42-1.46), still significant. | Trim-and-fill analysis suggested two missing studies and reduced the estimate to SMD 1.04 (95% CI 0.57 to 1.51). | Of the included randomized trials, two were rated high risk of bias and one low; four of the non-randomized studies were rated serious risk. | No included study evaluated patient-related (Kirkpatrick Level 4) outcomes, and most measured short-term results.
rundown: The review was prospectively registered in PROSPERO and followed PRISMA 2020. Two reviewers screened 121 records after de-duplication, assessed 21 full texts and included 12 studies from China, Egypt, Pakistan, Thailand, Australia, Turkey, Germany, Spain and Brazil. Four were randomized trials, and sample sizes ranged from 30 to 300.

Interventions were mostly VR systems with machine-learning haptic feedback for procedural training, and chatbot or large language model tools for clinical reasoning. Comparators included phantom-head training, lectures, videos and workshops. Skills covered endodontics, operative skills, cavity preparation, history taking and diagnosis.

In the narrative synthesis, psychomotor gains were most consistent with VR-haptic and tutoring systems measured by objective metrics. Generative AI was more often tied to perceived gains in confidence and satisfaction than to validated improvements in knowledge. Higher-quality studies tended to report objective improvements, while lower-quality studies relied on subjective outcomes.

Meta-regression found that AI modality explained all the between-study heterogeneity, although the overall moderator test was not significant (p = 0.0773). Compared with generative AI, tutoring systems (β = 1.20) and VR simulation (β = 0.82) showed larger effects. The authors call these findings exploratory.
sources:
- peer_reviewed | BMC Medical Education | https://doi.org/10.1186/s12909-026-09390-6 | 2026-05-13
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