• Comparing AI-Assisted Problem-...

Treffer: Comparing AI-Assisted Problem-Solving Ability With Internet Search Engine and e-Books in Medical Students With Variable Prior Subject Knowledge: Cross-Sectional Study.

Title:
Comparing AI-Assisted Problem-Solving Ability With Internet Search Engine and e-Books in Medical Students With Variable Prior Subject Knowledge: Cross-Sectional Study.
Authors:
Xavier A; Department of Pharmacology, Jawaharlal Nehru Medical College and Hospital, Aligarh Muslim University, Medical Road, Aligarh, Uttar Pradesh, 202001, India, 91 9634912166., Naeem SS; Department of Pharmacology, Jawaharlal Nehru Medical College and Hospital, Aligarh Muslim University, Medical Road, Aligarh, Uttar Pradesh, 202001, India, 91 9634912166., Rizwi W; Department of Pharmacology, Jawaharlal Nehru Medical College and Hospital, Aligarh Muslim University, Medical Road, Aligarh, Uttar Pradesh, 202001, India, 91 9634912166., Rabha H; Department of Pharmacology, Jawaharlal Nehru Medical College and Hospital, Aligarh Muslim University, Medical Road, Aligarh, Uttar Pradesh, 202001, India, 91 9634912166.
Source:
JMIR medical education [JMIR Med Educ] 2026 Jan 06; Vol. 12, pp. e81264. Date of Electronic Publication: 2026 Jan 06.
Publication Type:
Journal Article; Comparative Study
Language:
English
Journal Info:
Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101684518 Publication Model: Electronic Cited Medium: Internet ISSN: 2369-3762 (Electronic) Linking ISSN: 23693762 NLM ISO Abbreviation: JMIR Med Educ Subsets: MEDLINE
Imprint Name(s):
Original Publication: Toronto, ON : JMIR Publications, [2015]-
MeSH Terms:
Students, Medical*/psychology , Students, Medical*/statistics & numerical data , Search Engine* , Problem Solving* , Internet* , Artificial Intelligence* , Pharmacology*/education, Cross-Sectional Studies ; Humans ; Male ; Female ; Educational Measurement/methods ; Education, Medical, Undergraduate/methods ; Young Adult
References:
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Contributed Indexing:
Keywords: AI; ChatGPT; LLM; artificial intelligence; cognitive performance; large language models; medical education; subject-naive learners
Entry Date(s):
Date Created: 20260106 Date Completed: 20260106 Latest Revision: 20260109
Update Code:
20260109
PubMed Central ID:
PMC12772426
DOI:
10.2196/81264
PMID:
41493542
Database:
MEDLINE

Weitere Informationen

Background: Artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT (OpenAI), is rapidly influencing medical education. Its effectiveness for students with varying levels of prior knowledge remains underexplored.
Objective: This study aimed to evaluate the performance of medical students with and without formal pharmacology knowledge when using AI-LLM GPTs, internet search engines, e-books, or self-knowledge to solve multiple-choice questions (MCQs).
Methods: A cross-sectional study was conducted at a tertiary care teaching hospital with 100 medical students, divided into a "naive" group (n=50; no pharmacology training) and a "learned" group (n=50; completed pharmacology training). The study was started after approval from the Institutional Ethics Committee of Jawaharlal Nehru Medical College Hospital, Aligarh Muslim University (1018/IEC/23/8/23). Each participant answered 4 sets of 20 MCQs using self-knowledge, e-books, Google, or ChatGPT-4o. Scores were compared using analysis of covariance with self-knowledge scores as a covariate.
Results: Learned students significantly outperformed naive students across all methods (P<.001), with the largest effect size in the AI-LLM GPT set (partial η²=0.328). For both groups, the performance hierarchy was AI-LLM GPT > internet search engine > self-knowledge ≈ e-books. Notably, the naive students who used AI scored higher (mean 13.24, SD 3.31) than the learned students who used Google (mean 12.14, SD 2.01; P=.01) or e-books (mean 10.22, SD 3.12; P<.001).
Conclusions: AI-LLM GPTs can significantly enhance problem-solving performance in MCQ-based assessments, particularly for students with limited prior knowledge, even allowing them to outperform knowledgeable peers using traditional digital resources. This underscores the potential of AI to transform learning support in medical education, although its impact on deep learning and critical thinking requires further investigation.
(© Ajiith Xavier, Syed Shariq Naeem, Waseem Rizwi, Hiramani Rabha. Originally published in JMIR Medical Education (https://mededu.jmir.org).)