Exploring Factors Influencing Physicians’ Teaching Motivation in the Era of Artificial Intelligence: An Explanatory Mixed-Methods Study

Abstract

Background: Physicians’ motivation for teaching plays a pivotal role in the quality of clinical education and the preparation of future physicians. As artificial intelligence (AI) becomes increasingly integrated into medical education, the factors shaping teaching motivation are evolving. This study aimed to identify factors influencing physicians’ teaching motivation in the era of AI. Methods: This explanatory mixed-methods study was conducted concurrently in 2025. In the quantitative phase, 72 physicians were selected using stratified random sampling, and data were collected using a researcher-developed questionnaire. The content validity of the instrument was assessed using the Content Validity Ratio (CVR) and Content Validity Index (CVI), while its reliability was established using Cronbach’s alpha. Quantitative data were analyzed using SPSS version 21 and the Friedman test. In the qualitative phase, 20 physicians were selected through nested sampling, and data were collected using open-ended questions and semi-structured interviews. Qualitative sampling and data collection continued until data saturation was achieved, defined as the point at which subsequent interviews yielded no new information or concepts. Qualitative data were analyzed using the inductive content analysis approach proposed by Elo and Kyngäs. Finally, findings from the two phases were integrated at the interpretation stage. Results: Physicians’ teaching motivation was influenced by individual, educational, organizational, and technological factors. The Friedman test revealed significant differences among the motivational dimensions (χ² = 212.373, df = 8, p < .001). Teaching competence (4.46 ± 0.44), reward and incentive systems (4.45 ± 0.59), and personal interest and motivation (4.44 ± 0.46) received the highest mean scores, whereas workload and responsibilities (3.77 ± 0.95) and educational policies and structures (3.90 ± 0.60) were rated lower. Qualitative findings highlighted the importance of pedagogical skills training, fairness in reward systems, positive student interactions, supportive educational environments, and AI-based technologies in facilitating teaching and reducing workload. Participants also emphasized that intelligent tools could improve efficacy through enhanced feedback, instructional design, and management of repetitive tasks. Conclusions: Physicians’ teaching motivation is multidimensional and shaped by the interplay of individual, educational, organizational, and technological factors. The findings suggest that strengthening physicians’ motivation to teach requires simultaneous attention to their individual capacities and the conditions within the educational environment. From this perspective, creating supportive conditions for physicians’ engagement in educational activities may provide a more conducive context for sustaining and strengthening their educational role.