Developing Interdisciplinary Competence in AI and New Materials at Application-Oriented Universities

Qiu Hong, Wang Renfang, Hu Haoliang, Yang Jun, Huang Yingying^*

College of Big Data and Software Engineering, Zhejiang Wanli University

Abstract：

Integrating Artificial Intelligence (AI) into materials education requires more than combining disciplinary courses. Graduate students need to connect computational methods with materials knowledge and practical constraints. This conceptual paper proposes an interdisciplinary graduate education framework for application-oriented universities, with Zhejiang Wanli University serving as the primary context for its design. Drawing on constructive alignment, boundary-crossing perspectives, and documented graduate training practices, the framework integrates curriculum design, supervised projects, faculty development, regional resources, and continuous feedback. It emphasizes shared assessment criteria, interdisciplinary interpretation of project work, and coordinated review as key mechanisms for supporting interdisciplinary competence. An illustrative interdisciplinary project is used to show how these mechanisms can be embedded in project activities and assessment. The paper further proposes an evaluation agenda for future empirical examination, particularly regarding student preparation, mentoring capacity, and the balance between industrial relevance and educational autonomy.

Key Words：

interdisciplinary competence; graduate education; constructive alignment; boundary crossing; Artificial Intelligence; materials informatics