Evidence map›Paper›PMID 41094676›Full record

ArticleBMC medical education2025

Leveraging ChatGPT to support terminology learning in oral anatomy: a mixed-methods study among linguistically diverse dental students.

Avita Rath

Abstract read
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Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Avita RathFaculty of Dentistry, SEGi University , No. 9 Jalan Teknologi, PJU 5, Kota Damansara , Petaling Jaya, Selangor, 47810, Malaysia. drrathavita@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEnglish-medium instruction (EMI) in health professional education presents linguistic barriers for non-native English-speaking students, particularly in terminology-dense subjects like dental anatomy. While artificial intelligence (AI) has shown promise in educational contexts, its application to support terminology learning among multilingual learners remains underexplored. This study evaluated the impact of ChatGPT-assisted learning on terminology comprehension and student engagement among international dental students in Malaysia.

methodsA convergent mixed-methods pilot study was conducted with 35 Chinese international Year 1 and Year 2 dental students enrolled in an oral anatomy course at SEGi University. Students completed pre- and post-intervention comprehension tests and engagement surveys. Over an eight-week period, students used ChatGPT to clarify terminology and explore anatomical content. ChatGPT usage logs were analyzed, and post-intervention focus group discussions were conducted. Quantitative data were analyzed using paired t-tests and Wilcoxon Signed-Rank Tests; qualitative data underwent thematic analysis.

resultsComprehension scores improved significantly post-intervention (mean increase = 5.7 points, p< 0.001; Cohen’s d = 1.91). Engagement scores also increased significantly (p < 0.001), reflecting enhanced autonomy and motivation. ChatGPT was used primarily for term clarification (45%), function explanation (32%), and structural comparisons (23%). Thematic analysis revealed four key themes: (1) Clarity and Confidence, (2) Self-Directed Learning, (3) Concerns About Accuracy and Overreliance, and (4) Ethical and Privacy Awareness.

conclusionsAI-assisted terminology learning significantly improved comprehension and engagement among linguistically diverse dental students, aligning with principles from Cognitive Load Theory (CLT) and Self-Determination Theory (SDT). However, ethical concerns—particularly around data privacy and unchecked reliance—highlight the importance of faculty guidance and institutional oversight. These findings underscore the value of hybrid AI-human approaches that are pedagogically sound, ethically responsible, and culturally adaptive.

Indexed as

AnatomyEducation, DentalMultilingualismStudents, DentalTerminology as TopicComprehensionFemaleFocus GroupsGenerative Artificial IntelligenceHumansLarge Language ModelsLearningMalaysiaMalePilot ProjectsArtificial intelligenceChatGPTDental educationEnglish-medium instructionEthics in AIMultilingual learnersSelf-directed learningStudent engagementTerminology comprehension

Identifiers

PMID41094676
PMCPMC12522743

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.