The potentials and challenges of integrating generative artificial intelligence (AI) in dental and orthodontic education: a systematic review
Bibliographic record
Abstract
BACKGROUND: Generative AI technologies offer significant opportunities to enhance orthodontic education by improving knowledge retention, clinical decision-making, and skills training. This systematic review aimed to evaluate the impact of generative AI tools in orthodontic education, focusing on knowledge retention, decision-making, and practical skills. METHODS: A comprehensive literature search was conducted across PubMed, Cochrane Library, ERIC, CINAHL, and IEEE Xplore from January 2010 to December 2023. Studies evaluating the integration of generative AI in dental and orthodontic education were included. Seventeen studies met the inclusion criteria. Risk of bias was assessed using the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale, with the GRADE approach used to evaluate evidence quality. RESULTS: Generative AI improved knowledge retention and clinical decision-making through adaptive learning pathways and real-time feedback. Barriers included limited faculty training, technical infrastructure deficits, and educator resistance. CONCLUSIONS: Generative AI holds transformative potential for orthodontic education but requires addressing practical and ethical challenges. Future research should focus on longitudinal studies to validate long-term impact and explore integration strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".