Actionable companion · Motivation & engagement

From AI evidence to responsible practice: Large language model use in dental education: a cross-sectional multi-country study

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings 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): Higher education. Health-professions education findings do not establish effectiveness in K–12 classrooms.

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

Abstract Background Large language models (LLMs) are increasingly used in higher education, but multi-country evidence on dental students’ use, verification, and integrity practices is limited. Objective To compare senior dental students’ LLM use, perceived time and academic impact, reliability judgements, verification practices, and integrity safeguards across five countries. Methods An anonymous cross-sectional online survey was administered to final-year dental students in the United Arab Emirates (UAE), Jordan, Malaysia, Oman, and Brazil. Measures included tools used, frequency and motivations, learning activities, perceived time and academic impact, verification frequency and strategies, guideline awareness, and integrity safeguards. Analyses used Kruskal-Wallis and chi-square tests with Benjamini-Hochberg adjustment, effect sizes, Spearman correlations, and ordinal logistic models. Results In total, 454 students participated (UAE 160, Jordan 101, Malaysia 75, Oman 62, Brazil 56; mean age 22.9; 74.9% female). ChatGPT predominated (95.9%), followed by Gemini, formerly Bard (18.0%), DeepSeek (16.4%), and Claude (7.4%). Tool diversity varied across country-based cohorts, with Oman showing greater multi-tool uptake. Use was frequent (several times/week 39.2%, daily 28.6%). Key motivations were saving time (73.0%), clarifying concepts (56.9%), and summarising (54.1%). Common activities included understanding complex concepts (75.3%), summarising lecture notes (70.0%), exam preparation (61.5%), and assignment research (53.2%); exam-time assistance was reported by 25.6%. Verification was ‘always’ 20.0% and ‘often’ 34.1%, varying across country-based cohorts, with Oman verifying less frequently than other cohorts. Guideline awareness was 40.3% overall (UAE 61.3% vs Brazil 8.3%). Integrity safeguards commonly involved paraphrasing (69.6%), citations (39.2%), and plagiarism checks (38.0%); disclaimers were uncommon (9.2%). LLM-use frequency correlated with broader academic use (ρ = 0.289) but not with integrity concern (OR = 0.963). Conclusions LLM use is widespread and heterogeneous across settings, including non-trivial higher-stakes use. Dental programmes should implement explicit training in verification, evidence traceability, and disclosure, supported by clear, enforceable guidance and assessment designs aligned with real-world LLM practices.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Higher education. 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.

Participant ages · source passage

Results In total, 454 students participated (UAE 160, Jordan 101, Malaysia 75, Oman 62, Brazil 56; mean age 22.9; 74.9% female).

Study methods · source passage

A cross-sectional questionnaire-based study was conducted using an anonymous online survey administered to senior undergraduate dental students across five countries: the United Arab Emirates (UAE), Jordan, Malaysia, Oman, and Brazil. Participating institutions were identified through the research team’s academic networks. The participating institutions were therefore treated analytically as country-based institutional cohorts rather than nationally representative samples. The survey was disseminated across six dental institutions, with two institutions in the UAE and one institution in each of Jordan, Malaysia, Oman, and Brazil. The survey link was shared through local institutional channels. Participation was voluntary and no identifying information was collected.

Limitations and uncertainty · source passage

Early scholarly and policy-oriented discussion around LLMs has understandably centred on assessment integrity, academic misconduct and potential risks to critical thinking and independent judgement [4,6,9]. However, multiple reviews and surveys indicate that empirical evidence is still developing, uneven across contexts and often dominated by cross-sectional, single-setting or single-country samples, which limits what can be inferred about generalisable versus context-specific patterns [2,10]. In parallel, analyses of institutional AI documents suggest that institutional responses have frequently prioritised academic honesty language over more comprehensive implementation and review mechanisms, contributing to variability in what students are told and how use is governed [6–9].

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

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.

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. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. 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. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. 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

Abubaker Qutieshat; Lovely M Annamma; Gurdeep Singh; Mohd Hafiz Arzmi; Wan Nurhazirah Wan Ahmad Kamil; Lina Khasawneh; Sudhir Rama Varma; Jair Carneiro Leão; Biji Thomas George; Mohammad S Alrashdan. Large language model use in dental education: a cross-sectional multi-country study. Medical Education Online (2026-08-06). https://doi.org/10.1080/10872981.2026.2707729

Companion prepared 2026-11-01. 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.