Evidence map›Paper›PMID 41691269›Full record

ArticleBMC medical education2026

Implementation of an artificial intelligence-powered training platform in oral medicine and pathology education: a comparative study.

Bernadette Quah, Nicole Wan Chyan Chia, Mai Khanh Ha Huynh, Intekhab Islam

Abstract readComparative Study
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Bernadette QuahFaculty of Dentistry, National University of Singapore, Singapore, Singapore. b_quah@nus.edu.sg.
Nicole Wan Chyan ChiaMinistry of Health Holdings Pte Ltd, Singapore, Singapore.
Mai Khanh Ha HuynhMed2Lab, Inc. - San Francisco, San Francisco, CA, USA.
Intekhab IslamFaculty of Dentistry, National University of Singapore, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to evaluate the levels of student acceptance and effectiveness of an AI-powered chatbot platform as a teaching tool in the undergraduate oral medicine and pathology curriculum compared to traditional teaching methods.

methodsOral medicine and pathology case scenarios were developed with prompt engineering techniques on the GPT-4-powered CognitusAI web-based platform (Med2Lab), which utilised both a virtual patient and a virtual preceptor in conducting each case. The first phase of the study involved pilot testing to assess 50 students’ levels of acceptance of the platform and gather suggestions for improvement. The second phase was a comparative study, with 40 students in the intervention group undergoing case-based learning on the CognitusAI platform, and 40 in the control group undergoing a traditional tutorial of the same scenarios. The students took pre- and post-intervention assessments to evaluate knowledge levels.

results92% of students felt confident to apply the knowledge gained on the platform, and 86% stated that it encouraged them to understand. Over 80% of students were more aware of their progress and areas for improvement after the module, and over 90% were interested in similar learning opportunities in the future. Students felt that the platform’s advantages included its strong clinical relevance, facilitation of active recall, personalised and non-judgemental feedback, and good engagement and interactiveness. In the comparative study, students in both the intervention and control groups showed significant score improvements after the teaching session (p < 0.001 for both). Although the difference in score improvements between the intervention and control group was not statistically significant (p = 0.062), the intervention group had significantly higher post-intervention test scores (p = 0.041) and a higher proportion of students with improved scores (p < 0.001).

conclusionAI-powered chatbot platforms are well-received teaching tools and are equally effective and potentially superior to traditional teaching methods in enhancing students’ knowledge and understanding in oral medicine and pathology.

Indexed as

Artificial IntelligenceComputer-Assisted InstructionEducation, DentalOral MedicinePathologyCurriculumEducational MeasurementFemaleHumansIntelligent SystemsMaleProblem-Based LearningArtificial intelligenceEducation, dentalOral medicinePathology, oralSurgery, oral

Identifiers

PMID41691269
PMCPMC13011764

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.