Medical student reliance on artificial intelligence in nephrology education.

Authors

Amrit Kirpalani, Peter Zhan Tao Wang

Published in

Journal of nephrology. Jul 16, 2026. Epub Jul 16, 2026.

Abstract

Large language models such as ChatGPT are increasingly used in medical education and may influence student learning and decision-making. Despite strong performance on factual recall, limitations in clinical reasoning raise concerns about how learners engage with artificial intelligence (AI)-generated recommendations, particularly in challenging domains such as renal physiology, where foundational understanding underpins clinical application.
Fifty-seven first-year medical students completed 24 multiple-choice questions during a pediatric nephrology and urology case-based learning session. Each clinical question was paired with a corresponding foundational science question and presented in random order. Students answered individually, reviewed a ChatGPT-generated answer (deliberately correct or incorrect), and then re-answered the question. Change-to-match behavior was recorded. Response-level analyses used generalized estimating equation logistic regression to account for repeated measures within students, with sensitivity analyses adjusting for item difficulty.
A total of 1357 paired responses were analyzed. Students changed their answers to match ChatGPT in 22.3% of cases. Change-to-match behavior was more frequent for foundational than clinical questions (24.1% vs. 20.4%). After adjusting for clustering and item difficulty, foundational question type remained associated with greater likelihood of change-to-match behavior (odds ratio [OR] 1.57, 95% confidence interval [CI] 1.08-2.29). Incorrect-to-correct answer changes exceeded correct-to-incorrect changes, resulting in a modest net improvement in accuracy.
Medical students selectively relied on AI-generated recommendations, particularly when uncertain and when engaging with foundational content. These findings suggest that reliance on AI is context-dependent and highlight the importance of domain-sensitive AI literacy in nephrology education.

PMID:
42460549
Bibliographic data and abstract were imported from PubMed on 16 Jul 2026.

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