Platform-driven multidisciplinary integration for graduate medical imaging education: a theoretical framework based on knowledge integration framework and technology acceptance model 2

Graduate education in medical imaging is undergoing a paradigm shift from discipline-specific knowledge transmission toward the cultivation of multidisciplinary integration competencies. With the increasing convergence of imaging science, clinical medicine, engineering, and artificial intelligence (AI) in clinical practice, traditional training models that rely on fragmented, discipline-based curricula are insufficient to prepare graduate students for solving complex interdisciplinary problems. Although emerging educational technologies and learning platforms have been developed to promote interdisciplinary training, their effectiveness depends not only on the design of cognitive learning pathways but also on learners' acceptance and sustained engagement with these technologies; however, current educational frameworks rarely integrate cognitive mechanisms and technology acceptance factors within a unified model. Therefore, this study proposes a platform-driven, dual-theory integrated teaching framework to support multidisciplinary competency development in medical imaging graduate education from both cognitive and technological perspectives.

This study constructed a theoretical framework by integrating the knowledge integration framework (KIF) and technology acceptance model 2 (TAM2). The four cognitive processes of KIF-elicitation, addition, differentiation, and organization-were mapped onto four corresponding platform functions: activation of prior knowledge, exposure to industrial and clinical perspectives, authenticity-based knowledge verification, and multidisciplinary integrated output. Based on TAM2, design strategies for targeting perceived usefulness and perceived ease of use were incorporated to explain the mechanisms underlying learners' acceptance, engagement, and utilization of the platform. By integrating KIF and TAM2, the framework established a dynamic learning pathway characterized by "knowledge activation-knowledge externalization-multidisciplinary integration-knowledge internalization".

The proposed KIF-TAM2 framework demonstrated conceptual feasibility through theoretical modeling. However, its effectiveness in improving multidisciplinary learning outcomes requires further validation through empirical educational studies.

This study provides a theory-driven framework for designing multidisciplinary platform-based medical imaging education and offers a foundation for future empirical investigations into technology-enhanced graduate training.