Chinese Journal of Medical Education

Chinese Journal of Medical Education ›› 2026, Vol. 46 ›› Issue (3): 199-205.DOI: 10.3760/cma.j.cn115259-20250506-00503

• Educational Technologies • Previous Articles     Next Articles

A study on the influencing factors of the use of generative artificial intelligence by clinical medicine students

Chen Lihua1, Liu Shuo2, Xin Siyan3, Wang Yu4, Wu Hongbin5   

  1. 1Master Degree Candidate, Department of Health Policy and Management, Enrolled in 2023, School of Public Health, Peking University, Beijing 100191, China;
    2Master Degree Candidate, Department of Health Policy and Management, Enrolled in 2024, School of Public Health, Peking University, Beijing 100191, China;
    3Education Department, Beijing CHAO-YANG Hospital, Capital Medical University, Beijing 100024, China;
    4Master Degree Candidate, Department of Medical Education, Enrolled in 2023, School of Education, Peking University, Beijing 100871;
    5Institute of Medical Education, Peking University, Beijing 100191, China
  • Received:2025-05-06 Online:2026-03-01 Published:2026-02-28
  • Contact: Wu Hongbin, Email: wuhongbin@pku.edu.cn
  • Supported by:
    Undergraduate Teaching Reform Project of Peking University in 2025(JG2025170)

PDF (PC)

19

Abstract: Objective To explore the factors influencing the use of generative artificial intelligence (GenAI) among clinical medical students (Hereinafter referred to as medical students). Methods In 2024, the China medical student survey (CMSS) was used to investigate the use of GenAI among medical students. Descriptive statistics, chi-square tests, and logistic regression analysis were employed to examine the impact of personal, family, and institutional factors. Results Among the 81 892 medical students surveyed, 34 715 (42.4%) medical students reported using GenAI (ChatGPT, Ernie Bot, etc.) at least once a week. In terms of individual factors, male medical students had a significantly higher GenAI usage rate than females (OR=1.561, P<0.001); Second-year medical students demonstrated higher GenAI usage rate compared to their third-, fourth-, and fifth-year counterparts (OR=0.643, 0.406, and 0.423 respectively, all P<0.001; only child medical students showed a lower GenAI usage rate than their non-only child peers (OR=0.947, P=0.001). Regarding familial factors, medical students whose parents are engaged in medical-related professions show a higher GenAI usage rate than those whose parents are not (OR=1.074, P<0.001); medical students from households with an annual income exceeding 150 000 RMB show a higher GenAI usage rate than those from households with an annual income 150 000 RMB or less (OR=1.155, P<0.001); medical students from urban households exhibit a higher GenAI usage rate than those from rural households (OR=1.058, P=0.001). Regarding institutional factors, medical students from “Double First-Class”universities (DFC, a Chinese government initiative to build world-class universities and disciplines) demonstrated a higher GenAI usage rate than those from non-DFC universities (OR=1.194, P<0.001); medical students in universitial located in western China showed a significantily higher GenAI usage rate compared to those in universities in eastern China (OR=1.071, P<0.001), where is those in central China showed a significantily lower GenAI usage rate (OR=0.905,P<0.001); medical students from universities that offer AI courses show a significantly higher GenAI usage rate than those from universities without AI courses (OR=2.065, P<0.001). Conclusions The results of this study demonstrate that factors associated with a high usage rate of Generative AI (GenAI) among medical students include, but are not limited to, male gender, second-year standing, urban household background, an annual household income exceeding 150 000 RMB, enrollment in a Double First-Class university, and institutional offering of AI-related courses.Medical schools should consider integrating GenAI-focused medical courses to enhance medical students' adoption of these tools.

Key words: Generative artificial intelligence, Clinical medicine major, Medical student, Use behavior, Influencing factors

CLC Number: