Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Accuracy and response repeatability of three large language models on undergraduate operative dentistry multiple-choice questions

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

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

Introduction Artificial intelligence (AI) has emerged as a valuable tool in the field of dentistry to assist healthcare providers in diagnosis, optimize treatment outcomes, support research, and improve education. Furthermore, the generative aspects of AI have emerged as a powerful tool for dental professionals to tackle clinical and academic challenges. However, the reliability of AI in domains such as undergraduate training remains unverified. Aim This study aimed to evaluate the accuracy and consistency of three large language models (LLMs) in answering multiple-choice questions on the undergraduate operative dentistry curriculum to determine their reliability as a supplementary learning resource. Methods Sixty multiple-choice questions were formulated from the undergraduate operative dentistry curriculum. Each LLM (Claude, Gemini, and ChatGPT) was queried individually nine times over three days. The results were evaluated using a predetermined answer key. The accuracy of the LLMs was compared using a generalized estimating equation (GEE) with question-level clustering analysis. Consistency was assessed at the question level using response consistency, correctness consistency, and Fleiss' kappa. Statistical significance was set at P < 0.05. Results Gemini demonstrated the highest overall accuracy (98.89%), followed by ChatGPT (98.33%) and Claude (97.78%). No statistically significant difference in accuracy was observed among the three LLMs (GEE, overall Wald χ2(2) = 1.46, p = 0.481). All three models showed almost perfect intersession agreement (Fleiss' κ = 0.93–0.95), with response and correctness consistency ranging from 88.3% to 91.7% across models. Conclusions All three LLMs demonstrated high accuracy on operative dentistry MCQs, suggesting their potential utility as a supplementary educational tool. However, the residual error rates in the answers warrant the cautious integration of LLMs into dental education.

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

Jasmine Mary Antony; Nikhil Harikrishnan; Nishi Jayasheelan. Accuracy and response repeatability of three large language models on undergraduate operative dentistry multiple-choice questions. Frontiers in Dental Medicine (2026-08-17). https://doi.org/10.3389/fdmed.2026.1934573

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.