Reliability and Performance of Four Large Language Models in Orthodontic Knowledge Assessment
Bibliographic record
Abstract
Artificial intelligence-based large language models (LLMs) are gaining prominence as educational tools. This study evaluated the accuracy and reliability of four popular publicly available LLM models-ChatGPT 4.0, ChatGPT 4o, Google Gemini, and Microsoft CoPilot-in answering orthodontic questions from the National Board of Dental Examiners examinations. Each model was tested across three trials to assess response consistency. Reliability was analyzed using Cohen's and Fleiss' Kappa. Among the four tested models, Microsoft CoPilot demonstrated the highest reliability, while ChatGPT-4.0 had the highest accuracy. Variability across trials suggests that AI-generated responses remain inconsistent. The variable responses generated over time by LLMs limit their standalone applicability in orthodontic education. Older models at times outperformed newer models. AI model updates do not necessarily lead to improved reliability. Although AI models may show potential as supplementary study aids, their accuracy and stability require further refinement before being deployed in educational contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".