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From Lecture Slides to Personalized Assessments: ChatGPT-Driven Digestive Pathology Questions for Targeted Learning
Sompon Apornvirat1, Adiluck Pisutpunya2, Nawaluk Atiroj2
1Division of Pathology, Chulabhorn International College of Medicine, Thammasat University, Pathum Thani, Thailand.
Pathology International
|January 9, 2026
Summary
ChatGPT can create accurate multiple-choice questions (MCQs) from pathology slides. Expert review ensures quality, offering a scalable tool for medical education.
Area of Science:
- Medical Education Technology
- Artificial Intelligence in Pathology
- Digital Learning Tools
Background:
- Developing effective assessment tools is crucial for medical education.
- Traditional methods of creating multiple-choice questions (MCQs) can be time-consuming.
- Leveraging artificial intelligence offers potential solutions for educational content generation.
Purpose of the Study:
- To evaluate ChatGPT's capability in generating clinically integrated MCQs from digestive pathology lecture slides.
- To assess the educational quality of AI-generated MCQs through expert review, psychometric analysis, and student feedback.
Main Methods:
- Utilized ChatGPT-4o to generate 30 MCQs with clinical vignettes from 30 digestive pathology lecture slides.
- Engaged three pathologists and one gastrointestinal/hepatobiliary pathologist for expert evaluation.
- Conducted psychometric analysis (difficulty and discrimination indices, distractor quality) with 31 medical students.
- Analyzed reviewer and student feedback.
Main Results:
- 93.3% of generated MCQs were rated excellent by expert pathologists.
- Most questions exhibited acceptable psychometric properties (76.7% met difficulty, 93.3% met discrimination thresholds).
- Students reported enhanced clarity, relevance, and clinical integration of the AI-generated MCQs.
Conclusions:
- ChatGPT-4o can accurately generate clinically relevant MCQs aligned with lecture material.
- Expert review refines AI-generated MCQs, ensuring high educational quality.
- This AI-assisted approach offers a practical and scalable solution for pathology education, particularly in resource-limited settings.
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