Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0675
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-07T06:26:46.182982Z
status: published
lens: trace
sector: education
headline: Large language model use in dental education: a cross-sectional multi-country study
dek: 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, Malay…
gain_title: Final-year dental students reported frequent LLM use to save time and support learning, including clarifying and understanding complex concepts.
problem_title: A quarter of students reported exam-time assistance, while consistent verification was low and awareness of institutional guidelines was limited.
trace_subject: LLM use for academic learning and assessment among final-year dental students in five countries
gain_reading: Final-year dental students reported frequent LLM use to save time and support learning, including clarifying and understanding complex concepts.
gain_evidence: clarifying concepts (56.9%) | understanding complex concepts (75.3%)
problem_reading: A quarter of students reported exam-time assistance, while consistent verification was low and awareness of institutional guidelines was limited.
problem_evidence: exam-time assistance was reported by 25.6% | Verification was 'always' 20.0% | Guideline awareness was 40.3% overall
quick_read: A cross-sectional survey of 454 final-year dental students in the UAE, Jordan, Malaysia, Oman, and Brazil examined LLM use, motivations, and safeguards. Published August 6 2026, it found ChatGPT predominated at 95.9%, with 39.2% using LLMs several times per week and 28.6% daily for tasks like understanding complex concepts and summarising lecture notes.

The findings matter because frequent academic use coexists with higher-stakes use and uneven safety practices: 25.6% reported exam-time assistance, only 20.0% said they always verify outputs, and guideline awareness was 40.3% overall. The authors conclude programs need explicit training in verification, evidence traceability, and disclosure, but the survey design cannot establish causal effects on learning outcomes or long-term integrity.
limitation: 
tag: Automated dual reading
key_points: 454 final-year students surveyed across UAE, Jordan, Malaysia, Oman, and Brazil; 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%). | Use was frequent: several times/week 39.2%, daily 28.6%; motivations included saving time, clarifying concepts, and summarising. | Verification was 'always' 20.0% and 'often' 34.1%, varying across country-based cohorts, with Oman verifying less frequently.
rundown: The anonymous cross-sectional online survey of 454 final-year dental students found ChatGPT use at 95.9% with frequent use patterns and motivations centered on saving time and clarifying concepts. Common activities were understanding complex concepts, summarising lecture notes, exam preparation, and assignment research.

Results showed heterogeneity across country-based cohorts in tool diversity, verification frequency, and guideline awareness, which was 40.3% overall with UAE 61.3% vs Brazil 8.3%. Integrity safeguards relied on paraphrasing, citations, and plagiarism checks, while disclaimers were uncommon, and LLM-use frequency correlated with broader academic use but not with integrity concern.
sources:
- peer_reviewed | Medical Education Online | https://doi.org/10.1080/10872981.2026.2707729 | 2026-08-06
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

474583fbbef7092ba8a2612358bc83bd77fb9e1b50ed3e88782fb84c6e4e8083

previous

0000000000000000000000000000000000000000000000000000000000000000