In the era of artificial intelligence, the cultivation of interdisciplinary competence has emerged as a critical imperative for higher education. This study introduces a novel scale to assess students' interdisciplinary competence in AI-enabled learning environments. Through a rigorous three-phase development process, we propose to construct and validate a measurement instrument encompassing five dimensions: knowledge integration (the ability to synthesize and connect knowledge across disciplines with AI assistance), interdisciplinary thinking (the capacity to analyze problems from multiple disciplinary perspectives using AI tools), innovative application (the competence to create novel solutions through AI-enhanced cross-disciplinary approaches), problem solving (the capability to address complex issues by leveraging AI and interdisciplinary knowledge), and knowledge transfer (the ability to apply knowledge flexibly across different domains with AI support). The study plans to collect data from 900 graduate students across multiple disciplines at six universities in China, employing both exploratory and confirmatory factor analyses to validate the scale structure. This research aims to investigate how these interconnected competencies influence students' academic performance and innovation capabilities in AI-enabled contexts. The proposed instrument not only advances our theoretical understanding of interdisciplinary competence but also provides educators with a practical tool to evaluate and foster students' cross-disciplinary capabilities. The anticipated findings will carry important implications for curriculum design and pedagogical innovation in the age of artificial intelligence.

Authors:
Yanyi Wu, Zhejiang University, China
Chenghua Lin, Zhejiang University, China

About the Presenter(s)
Yanyi Wu is a research associate with the School of Public Affairs and the Institute of China's Science, Technology and Education Policy, Zhejiang University, Hangzhou, China.

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