Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background/Objectives: Artificial intelligence (AI) is entering dental curricula, yet its educational value in endodontics remains unclear. This review synthesized student-based evidence on AI in endodontics, primarily comparing AI vs. students on diagnostic tasks as an educational endpoint and secondarily considering assessment tasks relevant to training. Methods: PubMed/MEDLINE, Embase, Scopus, and Web of Science were searched in July 2025. Eligible studies involved dental students using AI in endodontic tasks or applied AI to student-generated outputs. For diagnostic comparisons we performed random-effects meta-analysis and a complementary Bayesian random-effects model with weakly informative priors. Risk of bias used QUADAS-2; certainty used GRADE. Results: Five studies met inclusion. Two provided complete mean–SD data for the primary meta-analysis and one contributed to a sensitivity model after SD imputation; two were summarized narratively (AUC/F1 only). Pooled effects favored AI: Hedges g = 1.48 (95% CI 0.60–2.36; I2 ≈ 84%); sensitivity (k = 3) g = 1.45 (95% CI 0.77–2.14; I2 ≈ 77%). Across the two LLM studies with analyzable means/SDs, the pooled mean difference in accuracy was approximately +20 percentage points (AI − students). Bayesian analyses yielded posterior means near 1.5 with 95% credible intervals excluding 0 and P (μ > 0) ≈ 1.00. Educational outcomes were sparsely and non-standardly reported. Conclusions: Student-based evidence indicates that AI likely outperforms dental students on endodontic diagnostic tasks, supporting its use as an adjunct for formative tutoring, objective feedback, and more consistent assessment. Keywords: endodontic education, artificial intelligence, dental students, large language models, convolutional neural networks, diagnostic accuracy, meta-analysis, Bayesian meta-analysis, educational assessment

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [16]; the completed PRISMA 2020 checklist is provided in the Supplementary Materials (Table S1). The protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO database CRD420251107002).

Limitations and uncertainty · source passage

Beyond absolute effect sizes, several parameters likely modulated the observed AI–student differences. First, the task domain matters—case-level diagnosis versus single-label condition classification and radiographic assessment (e.g., working-length estimation) impose different cognitive/measurement demands that can benefit models optimized for pattern recognition [40]. Second, in LLM settings, prompt design and the requirement for stepwise rationales can alter stability and accuracy, whereas unstructured prompts may inflate variance [41]. Third, the choice of metrics under potential class imbalance (accuracy vs. AUC/F1) can change conclusions and comparability across studies [42]. Finally, validation strategy and transparent reporting (internal vs. external validation, data shifts, and model/version specification) critically affect generalizability and reproducibility and should be standardized in endodontic education research using AI [43].

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
  • Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Carlos M Ardila; Eliana Pineda-Vélez; Anny M Vivares-Builes. Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence. Dentistry Journal (2025-10-23). https://doi.org/10.3390/dj13110489

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.