Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 22, 2025
Date Accepted: Feb 12, 2026
The Influencing Factors of Medical Postgraduates’ Usage Intention towards AIGC Tools in Academic Research: Qualitative Analysis
ABSTRACT
Background:
The application of artificial intelligence-generated content (AIGC) technology is becoming increasingly widespread across industries. In the field of scientific research, AIGC tools are also emerging as important auxiliary instruments for researchers.
Objective:
As postgraduates are the future backbone of the research team, cultivating their research abilities is crucial for the development of their respective industries. Investigating the factors influencing postgraduates' use of AIGC tools in scientific research is of great significance for promoting the effective and rational application of AIGC tools in academic field.
Methods:
Semi-structured interviews with 30 medical postgraduates were conducted, focusing on their scientific research behavior. Using grounded theory for coding, a scientific research behavior influencing factor model using AIGC tool is constructed.
Results:
It is found that performance expectancy, effort expectancy, social influence, facilitating conditions, individual characteristics, task characteristics, and technology characteristics are the seven factors affecting medical postgraduates' scientific research behaviors with AIGC tools. Moreover, performance expectancy mediates the relationship between task/technology characteristics and medical postgraduates' scientific research behaviors involving the utilization of AIGC tools, while task characteristics also moderate the impact of social influence and performance expectancy on these behaviors. Meanwhile, performance expectations play a mediating role in the relationship between task characteristics, technical characteristics, and the research behavior of medical graduate students using AIGC tools. Task characteristics can regulate the degree to which social influence and performance expectations affect the research behavior of medical graduate students using AIGC tools.
Conclusions:
Based on the analysis of influencing factors, strategies and recommendations were proposed: cultivating medical postgraduates' artificial intelligence literacy, optimizing AIGC service content, and establishing scientific research supervision systems.
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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.