ArticleJournal of dental sciences2025
Can a large language model create acceptable dental board-style examination questions? A cross-sectional prospective study.
Article in Journal of dental sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of large language models in dentomaxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2025Pooled it
- DentalEdu-AI graph: mapping evidence-grounded and knowledge-structured AI for dental education, assessment, and accuracy.The Saudi dental journal · 2026Article
- Guideline-based clinical reasoning in periodontology education: a comparative study of residents and large language models.BMC medical education · 2026Article
- Evaluation of GPT-4o and Gemini Advanced on the Korean National Dental Licensing Examination: Accuracy, consistency, and question generation.Journal of dental sciences · 2026Article
- Evaluating cognitive depth of AI-generated multiple-choice questions with Bloom's Taxonomy.PloS one · 2026Article
- Performance comparison of large language models on pediatric dentistry questions in the Turkish dentistry specialization examination.BMC medical education · 2025Article
- There are significant differences among artificial intelligence large language models when answering scientific questions.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Background/purpose: Numerous studies have shown that large language models (LLMs) can score above the passing grade on various board examinations. Therefore, this study aimed to evaluate national dental board-style examination questions created by an LLM versus those created by human experts using item analysis. Materials and methods: This study was conducted in June 2024 and included senior dental students ( Results: The response rate was 100%. The median difficulty indices of the LLM and human set were 55.00% and 50.00%, both within the range of "excellent" range. The median discrimination indices were 0.29 for the LLM set and 0.14 for the human set. Both sets had a median distractor efficiency of 80.00%. The differences in all criteria were not statistically significant ( Conclusion: The LLM can create national board-style examination questions of equivalent quality to those created by human experts.
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Registered trials
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