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Accuracy of responses were analyzed across the knowledge and cognitive process dimensions of the revised Bloom\u2019s taxonomy (RBT), as well as across subject areas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      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\u2019s chi-square test and Monte Carlo-corrected Fisher\u2019s exact test. Pairwise comparisons were carried out via Bonferroni-corrected Z-test. The results were presented as frequencies and percentages, and\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.050 was considered statistically significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Microsoft Copilot and Gemini 3 showed similar performance in answering MCQs, both models achieved higher accuracy than ChatGPT 5 and students (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). ChatGPT 5\u2019s overall accuracy was found to be significantly higher than that of the students. Accuracy of responses varied according to Bloom\u2019s taxonomy levels and subject areas. Microsoft Copilot exhibited over 90% accuracy in all categories of Bloom\u2019s 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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. 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