Abstract Background The aim of this study is to compare the performance of three large language models (ChatGPT 5, Microsoft Copilot, and Google Gemini 3), with that of dental students using their responses to multiple-choice questions (MCQs) in restorative dentistry. Accuracy of responses were analyzed across the knowledge and cognitive process dimensions of the revised Bloom’s taxonomy (RBT), as well as across subject areas. Methods The restorative dentistry exam questions used in this study were drawn from Turkish Dentistry Specialization Entrance Exam (DUS) administered between 2020 and 2025. The 90 five-option, single-best-answer MCQs were classified according to the RBT to ensure cognitive diversity. Following the exclusion of one exam question which had been annulled by the examination authority, the data analysis was performed on the remaining 89 exam questions. Accuracy of AI models and dental students was compared using Pearson’s chi-square test and Monte Carlo-corrected Fisher’s exact test. Pairwise comparisons were carried out via Bonferroni-corrected Z-test. The results were presented as frequencies and percentages, and p < 0.050 was considered statistically significant. Results Microsoft Copilot and Gemini 3 showed similar performance in answering MCQs, both models achieved higher accuracy than ChatGPT 5 and students ( p < 0.001). ChatGPT 5’s overall accuracy was found to be significantly higher than that of the students. Accuracy of responses varied according to Bloom’s taxonomy levels and subject areas. Microsoft Copilot exhibited over 90% accuracy in all categories of Bloom’s knowledge and cognitive process dimensions. At the application level of the cognitive process dimension, all chatbots descriptively achieved 100% accuracy; however, this subgroup difference did not reach statistical significance. Chatbot performance was generally superior to that of students across the subject areas of adhesive dentistry, dentin hypersensitivity, dental caries, tooth whitening, aesthetic restorative procedures, contemporary restorative materials, preventive dentistry, lasers, and saliva. Conclusion AI-based chatbots demonstrate considerable potential in answering questions about restorative dentistry. At the same time, the differences in performance observed among different models suggest there could be variation in the accuracy values of these systems depending on both the taxonomic level and the subject area of restorative dentistry. The findings support the potential use of chatbots as complementary learning resources in dental education. However, the reliability of such systems should be consistently verified and tested in both an academic and clinical setting under the direct supervision of experts. Moreover, students should be equipped with critical thinking skills to appropriately evaluate and use these systems.
Akkurt et al. (Mon,) studied this question.
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