From Lecture Slides to Personalized Assessments: ChatGPT‐Driven Digestive Pathology Questions for Targeted Learning

Abstract

This study evaluated ChatGPT's ability to generate clinically integrated multiple-choice questions (MCQs) directly from digestive pathology lecture slides and assessed the educational quality of the output using expert review, psychometric analysis, and student feedback. Lecture slides covering 30 digestive diseases were uploaded to ChatGPT-4o using a structured prompt to produce 30 MCQs, each with a clinical vignette, five answer choices, and explanations. Three pathologists and a gastrointestinal and hepatobiliary pathologist evaluated question quality. Psychometric analysis was conducted using difficulty index, discrimination index, and distractor quality based on responses from 31 s-year medical students. Reviewer and student feedback were also analyzed. Of the 30 MCQs, 28 (93.3%) were rated as excellent by all pathologists. Minor issues such as factual inaccuracies or overly leading clues were found in 11 questions (36.7%) and corrected. Most questions showed acceptable psychometric properties: 23 items (76.7%) met difficulty index thresholds, and 28 (93.3%) met discrimination index thresholds. Of 120 distractors, 54 (45%) were functional. Students reported improved clarity, relevance, and clinical integration. ChatGPT can generate accurate, clinically relevant MCQs aligned with lecture material. With expert review, this approach provides a practical and scalable tool for pathology education in resource- and time-limited settings.

Ähnliche Arbeiten