Actionable companion · Feedback & scaffolding

Learning with agency: The potentials and challenges of integrating generative artificial intelligence (AI) in dental and orthodontic education: a systematic review

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): Age / stage not established. Health-professions learning must be distinguished from K–12 instruction.

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

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. Keywords: Artificial intelligence, Machine learning, Dental education, Orthodontics, Clinical decision-making, Educational technology

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Evidence synthesis

Stage: Age / stage not established. 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 review is registered with PROSPERO (ID: CRD42024560484). This systematic review applied the PICO framework to guide study selection and evaluation, addressing the research question: How can generative AI (e.g., ChatGPT-4.0) be integrated into medical education, particularly in orthodontics, and what are the potential effects on educational outcomes and patient care? The population included medical and orthodontic educators, students, and practitioners engaged in educational or clinical training programs. The intervention comprised generative AI tools, including ChatGPT-4.0, virtual tutors, diagnostic assistants, and adaptive learning platforms. Traditional teaching methods, such as lectures, textbooks, and manual clinical training, were used as the comparator. The outcomes of interest included improvements in knowledge retention, clinical decision-making, and practical skill development, alongside broader implications for patient care quality. This structured approach established clear eligibility criteria and provided a robust foundation for synthesizing the findings of the review.

Limitations and uncertainty · source passage

This systematic review’s primary strength lies in its comprehensive and rigorous methodology, which included an extensive literature search and a structured selection process. However, certain limitations must be acknowledged. Screening and data extraction were conducted manually, which may introduce subjectivity, time constraints, and potential human error despite efforts to apply standardized criteria and independent assessments. The exclusion of non-English studies may have introduced language bias, potentially omitting relevant findings published in other languages. Additionally, the variability in study designs and methodologies among the included studies posed challenges to synthesizing findings effectively. Some non-randomized studies exhibited a moderate to high risk of bias, necessitating cautious interpretation of their results.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.
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

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.
Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
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.
Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Martin Baxmann; Krisztina Kárpáti; Zoltán Baráth. The potentials and challenges of integrating generative artificial intelligence (AI) in dental and orthodontic education: a systematic review. BMC Oral Health (2025-06-03). https://doi.org/10.1186/s12903-025-06070-7

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.