Evidence map›Paper›PMID 42472798›Full record

ArticleBMC oral health2026

Exploring the use of large language models for self-learning in preventive dentistry and their associations with academic performance: a cross-sectional pilot study among dental students.

Jiayi Yang, Mingming Li, Shuhua Wang, Zhuojin Shi, Jinhua Fang, Pei Zheng, Zhiwu Wu

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

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5 · Who and what money

Authors and funding

7 authors.

Jiayi Yang *School of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
Mingming Li *Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Shuhua WangSchool of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
Zhuojin ShiSchool of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
Jinhua FangSchool of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
Pei ZhengSchool of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, China. 20161040@zcmu.edu.cn.
Zhiwu WuSchool of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, China. wuzhiwu@zcmu.edu.cn.

Funding

the College Student Science and Technology Innovation Activity Program (Xinmiao Talent Program) of Zhejiang Province 2025R410A034the National Natural Science Foundation of China 82501131the Undergraduate Teaching Reform Project of Zhejiang Province JGBA2024775Undergraduate Teaching Reform Project of Zhejiang Chinese medical university YB24020
6 · The paper itself

Abstract

backgroundLarge language models (LLMs) are being gradually integrated into dental education, but more attention was paid in perspective of educators rather than students. Evidence regarding how students employ them for self-learning in Preventive Dentistry, and whether specific usage patterns are related to academic performance, remains scarce. This study examined dental students' self-perceived LLM knowledge, attitudes, and behaviors; evaluated perceived usefulness across Preventive Dentistry modules; and explored associations between LLM usage (frequency, type, motivation) and academic performance.

methodsThis cross-sectional pilot study recruited all third-year dental undergraduates enrolled in a Preventive Dentistry course at a university in eastern China. A total of 117 students provided valid responses and completed final examination. Academic performance was assessed using final examination scores as a continuous outcome variable. Linear regression analysis was performed to examine associations between LLM usage and examination scores. The data were analyzed using SPSS 22.0, and the level of significance was set at 0.05.

resultsLLM use was reported by all participants, with eighty-four students (71.8%) considering themselves as proficient users. For self-learning in Preventive Dentistry, 49.6% of participants reported using LLMs one to three times per month and DeepSeek (99.1%) was most popular. Students perceived LLMs as more helpful for theoretical learning than for practical skill acquisition (p < 0.001). The most frequently reported motivation for LLM use was to broaden knowledge and facilitate comprehension (76.1%), compared with other motivations. Importantly, the linear regression model (adjusted R

conclusionsIn this pilot sample, LLMs were widely reported to be used and were perceived by these students as more helpful for learning theoretical rather than for practical knowledge during self-learning in Preventive Dentistry. Using LLMs to broaden knowledge and facilitate comprehension was the most frequently reported motivation and showed a modest positive association with academic performance, although causality cannot be inferred due to the cross-sectional design. These findings provide preliminary insights into students' use of LLMs and may help inform future research on their role in dental education.

Indexed as

Academic PerformanceEducation, DentalLarge Language ModelsLearningPreventive DentistryStudents, DentalChinaCross-Sectional StudiesFemaleHumansMaleMotivationPilot ProjectsYoung AdultCross-sectional pilot studyDental studentsLarge Language Models (LLMs)Preventive DentistrySelf-learning

Identifiers

PMID42472798
PMCPMC13662992

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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.