Actionable companion · Self-regulation & metacognition
Learning with agency: Artificial Intelligence in Dental Education: Overview of Teaching, Assessment and Academic Performance Prediction
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.
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 Artificial Intelligence (AI) is increasingly used in dental education. The present study aimed to examine the current landscape of AI applications in dental education, focusing on their impact on teaching, assessment, and the prediction of academic performance. A literature search was performed based on articles published between November 2014 and March 2026 in the PubMed and Scopus databases. Studies focusing on AI applications in dental education were analysed across the domains: teaching and learning, assessment, and academic performance prediction. Forty-two publications met the inclusion criteria and were used as the evidential basis for this study. AI integration emphasised fundamental knowledge, use cases, and evaluation in teaching and learning. AI tools such as chatbots, simulations, and generative models enhanced student engagement and learning efficiency while supporting clinical decision-making. AI-assisted tasks, including radiograph interpretation and scientific writing, demonstrated improved outcomes. For assessment, AI showed moderate to high correlations with human evaluators for essay grading and thematic analysis. Further, AI models could forecast student outcomes, supporting personalised learning strategies and early intervention. AI applications have a vast potential to enhance dental education, particularly in improving teaching efficiency, personalised learning, self-directed learning, and assessment accuracy. Curriculum revisions and faculty training are necessary to fully integrate AI responsibly, leveraging its capabilities while maintaining the critical role of human oversight in education. Key words: Artificial Intelligence, Pedagogy, Dental Education, Health Professions Education
What kind of evidence is this?
Conversational & pedagogical agents · Qualitative / mixed methods
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
A study comparing feedback given by humans and AI for histology questions revealed that the AI-generated feedback was often detailed, clear, and precise, aligning well with the marking scheme. Examples were sometimes provided to aid student understanding. This contrasted with human feedback, which included motivational components that acknowledged students' efforts. Despite the clarity of AI feedback, students rated the grading accuracy and the feedback quality of human assessment higher than that of AI.19 However, the study design did not blind students to the types of feedback provided, which may have introduced bias favouring human-generated responses.
Limitations and uncertainty · source passage
The present study is subject to limitations related to its search terms and database selection. The literature search was conducted using only two databases, PubMed and Scopus, which may not have captured all potentially relevant articles. Nevertheless, these databases are widely recognised as principal sources for biomedical and interdisciplinary research, and using at least two databases is consistent with recommended practice to minimise the risk of overlooking eligible studies. In addition, the MeSH hierarchy term 'artificial intelligence' was employed in PubMed to encompass related sub-terms within the field. However, emerging terminology may not have been fully captured by this approach. Accordingly, future updated reviews should consider broader database coverage and more comprehensive search strategies to ensure a more complete identification of relevant publications.
Design the system before the pilot
Define whether the agent asks questions, gives hints, explains or assesses. Test hallucinated feedback, excessive praise, misleading confidence and responses when the learner is confused. A conversational interface alone does not establish autonomous agency.
- Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
- Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
- 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
Supachai Chuenjitwongsa; Tanit Arunratanothai; Paak Rewthamrongsris; Piyamas Sumrejkanchanakij; Kanoknadda Tavedhikul; Lakshman P Samaranayake; Thanaphum Osathanon. Artificial Intelligence in Dental Education: Overview of Teaching, Assessment and Academic Performance Prediction. International Dental Journal (2026-07-11). https://doi.org/10.1016/j.identj.2026.109733
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.