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Objective <p>This study aimed to evaluate the accuracy and consistency of responses provided by three large language models (LLMs), ChatGPT-5.2, Gemini-3, and DeepSeek-V3.2, to multiple-choice questions based on undergraduate endodontic education, asked on different days and at different times of the day.</p> Materials and methods <p>A total of 60 text-based multiple-choice questions were developed across six undergraduate endodontic topics: dental caries, pulpitis, apical periodontitis, periapical abscess, root fracture, and root resorption. Each question was presented to ChatGPT-5.2, Gemini-3, and DeepSeek-V3.2 at three time points per day (morning, afternoon, and evening) over four consecutive days. Accuracy and response consistency were analyzed using SPSS and R software, with statistical significance set at <i>p</i> &lt; 0.05 and a 95% confidence interval.</p> Results <p>ChatGPT-5.2 and Gemini-3 demonstrated significantly higher accuracy and consistency than DeepSeek-V3.2 (<i>p</i> &lt; 0.001 and <i>p</i> = 0.004, respectively). Model performance varied according to question category. Accuracy differed significantly across categories for ChatGPT-5.2 and Gemini-3, whereas consistency was influenced by question category only in ChatGPT-5.2. Model performance remained largely stable across different assessment times.</p> Conclusions <p>Advanced LLMs demonstrated promising performance in answering undergraduate endodontic multiple-choice questions and may serve as useful adjunctive tools in dental education. However, differences among models and variations in performance across topics highlight the need for critical evaluation of AI-generated responses before their educational use.</p>

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Performance of large language models on undergraduate endodontic multiple-choice questions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Meltem Sümbüllü,
  • Oğuzhan Ünal,
  • İlke Menteş,
  • Muzaffer Enes Kayahan

摘要

Objective

This study aimed to evaluate the accuracy and consistency of responses provided by three large language models (LLMs), ChatGPT-5.2, Gemini-3, and DeepSeek-V3.2, to multiple-choice questions based on undergraduate endodontic education, asked on different days and at different times of the day.

Materials and methods

A total of 60 text-based multiple-choice questions were developed across six undergraduate endodontic topics: dental caries, pulpitis, apical periodontitis, periapical abscess, root fracture, and root resorption. Each question was presented to ChatGPT-5.2, Gemini-3, and DeepSeek-V3.2 at three time points per day (morning, afternoon, and evening) over four consecutive days. Accuracy and response consistency were analyzed using SPSS and R software, with statistical significance set at p < 0.05 and a 95% confidence interval.

Results

ChatGPT-5.2 and Gemini-3 demonstrated significantly higher accuracy and consistency than DeepSeek-V3.2 (p < 0.001 and p = 0.004, respectively). Model performance varied according to question category. Accuracy differed significantly across categories for ChatGPT-5.2 and Gemini-3, whereas consistency was influenced by question category only in ChatGPT-5.2. Model performance remained largely stable across different assessment times.

Conclusions

Advanced LLMs demonstrated promising performance in answering undergraduate endodontic multiple-choice questions and may serve as useful adjunctive tools in dental education. However, differences among models and variations in performance across topics highlight the need for critical evaluation of AI-generated responses before their educational use.