Clinical Reasoning in Psychiatric Education: Development of Multiple-Choice Questions with Automatic Item Generation in Turkish
Authors: Esra Emekli, Emre Emekli, Yavuz Selim Kıyak, Yasemin Hoşgören Alıcı, Özlem Coşkun, Işıl İrem Budakoğlu
Why is this study important?
In this pioneering study published in the Turkish Journal of Psychiatry (2025), we demonstrated—for the first time—the feasibility of automatically generating Turkish case-based multiple-choice questions (MCQs) that assess clinical reasoning skills in psychiatry. Using a template-based Automatic Item Generation (AIG) approach and a Python-based algorithm, we developed 1,189 clinically grounded questions based on DSM-5 symptom clusters and evaluated them through expert review. This method offers a powerful, efficient solution to a longstanding challenge in medical education: generating large volumes of high-quality, reasoning-focused MCQs. It addresses the increasing need for case-based assessment in psychiatric training while reducing educators’ workload. Our findings also highlight the reliability, adaptability, and pedagogical value of AIG, especially in languages and fields where such applications have been scarce. This study lays the foundation for future applications of automated assessment tools in Turkish medical education—enhancing both teaching and learning by promoting higher-order thinking skills.
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Citation
Emekli, E., Emekli, E., Kıyak, Y. S., Hoşgören Alıcı, Y., Coşkun, Ö., & Budakoğlu, I. İ. (2025). Clinical reasoning in psychiatric education: Development of multiple-choice questions with automatic item generation in Turkish. Turkish Journal of Psychiatry, 36, Article 39. https://doi.org/10.5080/u27540
@article{emekli2025clinical,
author = {Emekli, Esra and Emekli, Emre and Kıyak, Yavuz Selim and Hoşgören Alıcı, Yasemin and Coşkun, Özlem and Budakoğlu, Işıl İrem},
title = {Clinical reasoning in psychiatric education: Development of multiple-choice questions with automatic item generation in Turkish},
journal = {Turkish Journal of Psychiatry},
year = {2025},
volume = {36},
pages = {Article 39},
doi = {10.5080/u27540},
url = {https://doi.org/10.5080/u27540}
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