From leadership research to district action: Artificial Intelligence in Dental Education: A Scoping Review of Applications, Challenges, and Gaps

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: review-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Background/Objectives: This scoping review aims to map existing AI applications in dental education, in student learning, assessment, and diagnostic training, identifying key limitations and challenges. Methods: Following the Arksey and O’Malley framework and PRISMA-ScR guidelines, six databases were searched in March 2025 using combinations of the following search words: “dental education,” “artificial intelligence,” “machine learning,” and “student assessment”. Inclusion was limited to English-language empirical studies focused on dental student education. Of 547 identified studies, 17 met the inclusion criteria. They were categorized into four domains: (1) Preclinical Training, (2) AI in Clinical, Diagnostic Training, and Radiographic Interpretation, (3) AI as an Assessment Tool and Feedback System, and (4) AI in Content Generation for Dental Education. Results: AI has positively influenced various domains, enhancing procedural accuracy, diagnostic confidence, assessment efficiency, and content delivery. However, it struggles to assess nuanced competencies like dexterity and clinical judgment. The challenges faced include disparate definitions of AI, ethical and privacy concerns, model variability, and a deficiency of dental leadership in AI development. At present, most tools are engineered by computer scientists and may not align effectively with the priorities of dental education. Conclusions: AI holds significant potential to enhance dental education outcomes. However, to guarantee its relevance and reliability, it requires standard frameworks, ethical oversight, and clinician-led development. Future research should concentrate on implementing real-time AI-driven feedback systems during preclinical training and advocate for more precise definitions to support consistent AI application and evaluation in dental education.

Study methods

This review followed the five-stage scoping framework proposed by Arksey and O’Malley [13], further refined by Peters et al. [14]. This scoping review was conducted and reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) 2020 guidelines [15]. The review protocol was not registered. In line with standard scoping review methodology, no formal critical appraisal of included studies was performed, as the aim was to map the breadth of available literature rather than assess study quality.

Limitations

This review followed a rigorous PRISMA-ScR methodology; however, some relevant studies may have been missed due to database restrictions or keyword limitations [15] Many included studies had methodological constraints such as small sample sizes, unvalidated instruments, and limited generalizability [18,19,31] Furthermore, a reliance on student self-reported data introduces potential bias due to recall or social desirability factors [30]. Few studies explored faculty or curriculum designer perspectives, revealing a gap in understanding institutional readiness for AI integration [20,21,28]. Some methodological limitations should be acknowledged. The review was not registered in a protocol registry, and although multiple databases were searched, the inclusion was limited to English-language publications, which may have excluded relevant non-English studies. Additionally, no formal critical appraisal tool was used, and data extraction and bias assessment were conducted manually, which may introduce subjective interpretation despite independent review by two authors.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Choose one school improvement question with teachers. Protect a short shared planning cycle, agree an instructional change and use a common rubric for examining student work. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Review implementation fidelity, teacher time and independently scored student work against a recorded baseline. Examine access differences and competing explanations before attributing changes to leadership. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Mohammed El-Hakim; Robert Anthonappa; Amr Fawzy. Artificial Intelligence in Dental Education: A Scoping Review of Applications, Challenges, and Gaps. Dentistry Journal (2025-08-25). https://doi.org/10.3390/dj13090384

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.