ArticleMedical education online2026
Large language model use in dental education: a cross-sectional multi-country study.
Article in Medical education online, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge language models (LLMs) are increasingly used in higher education, but multi-country evidence on dental students' use, verification, and integrity practices is limited.
objectiveTo compare senior dental students' LLM use, perceived time and academic impact, reliability judgements, verification practices, and integrity safeguards across five countries.
methodsAn 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.
resultsIn 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).
conclusionsLLM 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.