The Influencing Factors of Medical Postgraduates’ Usage Intention Toward Artificial Intelligence–Generated Content Tools in Academic Research: Qualitative Analysis

Background: The integration of artificial intelligence–generated content (AIGC) tools into academic research offers transformative potential for enhancing productivity and innovation. However, within the highly regulated and ethically sensitive medical context, the use of AIGC is accompanied by significant challenges. Medical postgraduates, as the future vanguard of medical science, play a crucial role in the advancement of digital health, and their intention to use AIGC tools will significantly influence the use of these emerging technologies in medical research. Despite the growing popularity of AIGC tools, there remains a paucity of in-depth understanding of the factors driving or hindering medical postgraduates’ intention to use these tools in academic research. A clear comprehension of these influencing factors is essential to foster the responsible, effective, and sustainable integration of AIGC into medical research. Objective: This study aimed to systematically explore the key factors influencing medical postgraduates’ intention to use AIGC tools in academic research, with the goal of informing strategies to promote their ethical use and enhance scholarly research capabilities. Methods: We used a qualitative research design based on grounded theory. Semistructured interviews were conducted with 30 medical postgraduates across diverse specialties, all of whom had prior research experience and familiarity with AIGC tools. Participants were recruited purposively to ensure diverse perspectives. Data analysis followed a systematic coding process to inductively develop a conceptual model, which was further structured and interpreted through the theoretical lens of the Unified Theory of Acceptance and Use of Technology. Results: Our analysis identified 7 core factors directly shaping usage intention: performance expectancy, effort expectancy, social influence, facilitating conditions, individual characteristics, task characteristics, and technology characteristics. Further analysis revealed that performance expectancy acted as a mediating variable in the relationships between both task characteristics and technology characteristics and usage intention. Additionally, social influence moderated the relationship between task characteristics and performance expectancy. The research findings underscore that, while AIGC tools are valued for assisting daily research tasks, medical postgraduates’ intention to use them in academic research is influenced by technical deficiencies, high cognitive load, and the strict ethical risks and data governance requirements in the medical field. Conclusions: This study constructs a conceptual model aimed at elucidating the influencing factors of medical graduate students’ intention to use AIGC in academic research. Recommendations derived from the findings include (1) fostering artificial intelligence literacy and critical competency among medical postgraduates; (2) optimizing AIGC tools to better address domain-specific needs, accuracy, and security concerns prevalent in health research; and (3) establishing clear academic supervision and ethical governance mechanisms to ensure responsible use. These measures are essential to harness the potential of AIGC while safeguarding the rigor and integrity of medical academic research.