[Comparative study on the application of large language models in pre-exam learning for the Chinese dental licen-sing examination].

Authors

Xiao Pang, Chang Liu, Jiahao Fan, Yan Huang, Ya'nan Sun, Jiyuan Liu

Published in

Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology. Volume 44. Issue 4. Pages 614-624. Aug 01, 2026.

Abstract

This study aims to compare the performance of different large language models (LLMs) in pre-exam learning for the Chinese dental licensing examination, with a focus on evaluating their differences in answering, explanation, and teaching effectiveness, to provide a reference for the application of LLMs in dental education.
Three evaluation scenarios were designed: selecting correct answers, providing answer explanations, and adversarial testing. DeepSeek-R1, Qwen 2.5-MAX, Doubao 1.5 Pro, Xinghuo Spark-X1, ERNIE 4.0 Turbo, GPT-4o, and Huaxi Zhilian were selected for comparative testing. Evaluation metrics included accuracy, net accuracy, and pedagogical effectiveness.
In the scenario of selecting correct answers, all LLMs exceeded the passing threshold, with Huaxi Zhilian achieving the highest accuracy (84%). In the answer explanation scenario, Huaxi Zhilian demonstra-ted the highest net accuracy (92%), followed by DeepSeek-R1 (89%), among models. Regarding pedagogical effectiveness, Huaxi Zhilian ranked highest in relevance, practicality, and clarity, whereas GPT-4o led in conciseness, among the investigated LLMs. In adversarial testing, Huaxi Zhilian and DeepSeek-R1 exhibited the smallest declines in accuracy and net accuracy, respectively, among the tested models.
In pre-exam learning for the Chinese dental licensing examination, knowledge-enhanced LLMs specifically optimized for dentistry (e.g., Huaxi Zhilian) outperform reasoning LLMs pretrained on general Chinese corpora (e.g., DeepSeek-R1) and those primarily trained on English corpora (e.g., GPT-4o). However, the performance of all models declines under adversarial conditions. Future research should focus on addressing identified weaknesses to enhance the utility of LLMs in dental education further.

PMID:
42576767
Bibliographic data and abstract were imported from PubMed on 11 Aug 2026.

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