Currently submitted to: JMIR Medical Education
Date Submitted: Oct 14, 2026
Open Peer Review Period: Oct 15, 2026 - Dec 10, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Generative AI Use in Learning and Research Among Undergraduate Clinical Medical Students in China: A Task-Recall Qualitative Study
ABSTRACT
Background:
Background:
Undergraduate medical students increasingly use generative artificial intelligence (GenAI) for learning and research-related tasks. However, less is known about how they refine prompts, revise outputs, verify information, and negotiate boundaries within specific educational tasks.
Objective:
Objective:
This study aimed to explore how undergraduate clinical medical students in China described using GenAI in specific learning and research tasks, focusing on prompt refinement, output revision, verification, and ethical and cognitive boundary negotiation.
Methods:
Methods:
We conducted individual semistructured remote interviews via WeChat voice calls or telephone using a task-recall approach with 50 undergraduate clinical medical students recruited through purposive sampling, supported by social media outreach and peer referral. Participants varied in training year, sex, internship status, and self-reported GenAI use. Interviews were conducted in Mandarin Chinese, audio-recorded in full with participants’ permission, transcribed verbatim, deidentified, and analyzed using codebook-informed thematic analysis with iterative team discussion.
Results:
Results:
The analysis generated five interrelated themes: convenience-driven entry into AI use, iterative co-construction of outputs for medical learning, critical verification before trust, ethical and cognitive boundary negotiation, and perceived institutional support needs. These themes informed an interpretive process model that distinguished reported task practices from perceived institutional support needs. Participants described using GenAI under task pressure, refining prompts, adapting outputs, and checking claims and references against other sources. Verification could prompt further revision, while concerns about academic integrity, independent reasoning, and group responsibility shaped decisions about whether and how outputs were used. Participants also described the workload involved in checking plausible errors and requested clearer rules, task-based training, and tools with traceable sources.
Conclusions:
Conclusions:
Participants’ accounts suggest that GenAI use in medical learning involves iterative revision, verification, and boundary negotiation. The findings highlight verification and accountability as priorities for task-based AI literacy education. The model reflects reported practices and does not establish verification effectiveness or improvements in learning outcomes.
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