Can ChatGPT generate practice question explanations for medical students, a new faculty teaching tool?

Abstract

INTRODUCTION: Multiple-choice questions (MCQs) are frequently used for formative assessment in medical school but often lack sufficient answer explanations given time-restraints of faculty. Chat Generated Pre-trained Transformer (ChatGPT) has emerged as a potential student learning aid and faculty teaching tool. This study aims to evaluate ChatGPT's performance in answering and providing explanations for MCQs. METHOD: Ninety-four faculty-generated MCQs were collected from the pre-clerkship curriculum at a US medical school. ChatGPT's accuracy in answering MCQ's were tracked on first attempt without an answer prompt (Pass 1) and after being given a prompt for the correct answer (Pass 2). Explanations provided by ChatGPT were compared with faculty-generated explanations, and a 3-point evaluation scale was used to assess accuracy and thoroughness compared to faculty-generated answers. RESULTS: < 0.001). CONCLUSION: ChatGPT shows promise in assisting faculty and students with explanations for practice MCQ's but should be used with caution. Faculty should review explanations and supplement to ensure coverage of learning objectives. Students can benefit from ChatGPT for immediate feedback through explanations if ChatGPT answers the question correctly on the first try. If the question is answered incorrectly students should remain cautious of the explanation and seek clarification from instructors.

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