Accepted for/Published in: JMIR Human Factors
Date Submitted: Jul 31, 2025
Open Peer Review Period: Nov 20, 2025 - Jan 15, 2026
Date Accepted: Dec 4, 2025
Date Submitted to PubMed: Dec 8, 2025
(closed for review but you can still tweet)
Utilization of AI among Medical Students and Development of AI Education Platforms in Medical Institutions: A Cross-Sectional Study
ABSTRACT
Background:
Background:
The emergence of Artificial Intelligence (AI) is driving digital transformation and reshaping medical education in China. Numerous medical schools and institutions are actively implementing AI tools for case-based learning, literature analysis, and lecture support. This expanding application is concurrently fostering the adoption of localized AI platforms. Consequently, such platforms are positioned to become a dominant paradigm in the coming years.
Objective:
Objectives: The primary aim of this study was to investigate the current use of AI tools among medical students, including usage frequency, commonly used platforms, and purposes of use. The second aim was to explore students’ needs and expectations toward AI-powered medical education platforms by collecting and assessing student feedback, and to identify practical requirements across disciplines and academic stages to inform more effective platform design.
Methods:
Methods:
An anonymous online questionnaire was administered to assess AI usage in learning, student feedback on AI-powered medical education platforms, and expected functionalities. The survey was conducted from March 1 to May 31, 2025, using convenience sampling to target medical students from disciplines including clinical medicine, basic medicine, nursing, rehabilitation therapy, traditional Chinese medicine, public health, epidemiology, pharmacy, and medical engineering across Shanghai, China.
Results:
Results:
A total of 428 valid questionnaires were collected. The average frequency of AI-assisted learning among medical students was (5.06±0.10) times per week. Over 90% of students (388/428) used more than two AI tools in their daily tasks. The average satisfaction score with the AI education platforms at their schools was (72.23±21.84), with significant individual differences. Students from different disciplines, educational stages, and academic systems demonstrated different usage patterns and expectations for AI-powered medical education platforms.
Conclusions:
Conclusions:
AI technology is widely accepted by medical students and is extensively applied across various aspects of medical education. Significant differences are observed in usage patterns across disciplines, educational stages, and academic systems. Understanding the actual needs of students is crucial for the construction of AI-powered medical education platforms.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.