Agentic education · Learning science

Actionable companion · Assessment & reasoning

From AI evidence to responsible practice: Evaluating ChatGPT and Google Gemini Performance and Implications in Turkish Dental Education

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Health-professions education findings do not establish effectiveness in K–12 classrooms.

Original authors’ abstract, reproduced under the linked source license:

Artificial intelligence (AI) has emerged as a transformative tool in education, particularly in specialized fields such as dentistry. This study evaluated the performance of four advanced AI models - ChatGPT-4o (San Francisco, CA: OpenAI), ChatGPT-o1, Gemini 1.5 Pro (Mountain View, CA: Google LLC), and Gemini 2.0 Advanced, in the Turkish Dental Specialty Examination (DUS) for 2020 and 2021. A total of 240 questions, comprising 120 questions per year from basic and clinical sciences, were analyzed. AI models were assessed based on their accuracy in providing correct answers compared to the official answer keys. For the 2020 DUS, ChatGPT-o1 and Gemini 2.0 Advanced achieved the highest accuracy rates of 93.70% and 96.80%, respectively, with net scores of 112.50 and 115 out of 120 questions. ChatGPT-4o and Gemini 1.5 Pro followed with accuracy rates of 83.33% and 85.40%. For the 2021 DUS, ChatGPT-o1 again demonstrated the highest accuracy at 97.88% (115.50 net score), closely followed by Gemini 2.0 Advanced at 96.82% (114.25 net score). Overall, ChatGPT-4o and Gemini 1.5 Pro scored lower for 2021, achieving accuracy rates of 88.35% and 93.64%, respectively. Combining results from both years (238 total questions), ChatGPT-o1 and Gemini 2.0 Advanced achieved accuracy rates of 97.46% (230 correct answers, 95% CI: 94.62%, 100.00%) and 97.90% (231 correct answers, 95% CI: 94.62%, 100.00%), respectively, significantly outperforming ChatGPT-4o (88.66%, 211 correct answers, 95% CI: 85.43%, 91.89%) and Gemini 1.5 Pro (91.60%, 218 correct answers, 95% CI: 87.75%, 95.45%). Statistical analysis revealed significant differences among the models (p = 0.0002). Pairwise comparisons demonstrated that ChatGPT-4o underperformed significantly compared to ChatGPT-o1 (p = 0.0016) and Gemini 2.0 Advanced (p = 0.0007) after Bonferroni correction. The consistently high accuracy rates and narrow confidence intervals for the top-performing models underscore their superior reliability and performance in answering the DUS questions. Generative AI modules such as ChatGPT-01 and Gemini 2.0 have the potential to enhance dental board exam preparation through question evaluation. While the AI modules appear to outperform humans on DUS questions, the study raises a concern about the ethical uses of AI and the true justification and value of DUS examinations as dental competency examinations. A higher level of knowledge evaluation should be considered. This research contributes to the growing body of literature on AI applications in specialized knowledge domains and provides a foundation for further exploration of its integration into dental education.

What kind of evidence is this?

Supporting AI learning science · Model benchmark / tool evaluation

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This study evaluated the performance of four different artificial intelligence models in answering questions from the Turkish Dental Specialty Examination (DUS) administered in 2020 and 2021 [17,18].

Limitations and uncertainty · source passage

Recent studies have demonstrated that AI models such as ChatGPT 3.5 and 4 can achieve high accuracy rates on US dental exams like the Integrated National Board Dental Examination (INBDE), Dental Admission Test (DAT), and Advanced Dental Admission Test (ADAT), particularly in knowledge-based and comprehension questions [9]. However, these AI models face challenges, especially with questions involving complex figures or images, which are common in prosthodontic and restorative dentistry sections [10,11]. Despite these challenges, AI's potential to enhance dental education and examination processes is significant, suggesting a need for continued research and adaptation in educational practices to leverage AI's capabilities while addressing its limitations.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Have a subject specialist reproduce a small set of the paper’s tool-evaluation tasks using the documented model and prompt conditions. Keep this as a tool-reliability exercise until a separate learner study is designed. Require learners to attempt equivalent tasks independently before any AI comparison. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Report tool accuracy and error severity separately from learner outcomes. Measure learner understanding and transfer with unaided parallel tasks; model examination scores are not evidence of improved student learning. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Ipek Kinikoglu. Evaluating ChatGPT and Google Gemini Performance and Implications in Turkish Dental Education. Cureus (2025-01-11). https://doi.org/10.7759/cureus.77292

Companion prepared 2026-11-01. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.