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Interdisciplinary competence in AI-enabled learning: Scale development and validation
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AI
DOI：10.30191/ETS.202601_29(1).RP08.png)
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Abstract
En 中文
Artificial intelligence (AI) integration challenges traditional definitions and assessments of interdisciplinary competence. Addressing the limitations of existing instruments that inadequately capture crucial human-AI dynamics, ethical considerations, and adaptive capabilities, this study develops and validates the Interdisciplinary Competence in AI-Enabled Learning (ICAIL) scale. Grounded in a synthesis of theories on interdisciplinarity, dynamic capabilities, and human-AI collaboration, the scale was refined through expert review and pilot testing, then validated with 872 students from Chinese universities using Exploratory and Confirmatory Factor Analyses. Results confirmed a robust five-dimensional structure: Knowledge Connectivity, Critical Interdisciplinary Analysis, AI-Driven Innovation, Collaborative Problem Solving, and Adaptive Transfer. The scale demonstrated high internal consistency reliability and excellent model fit, effectively measuring key competencies vital in AI-rich environments, such as critically evaluating AI outputs, engaging in iterative co-creation with AI, and strategically adapting tool usage. The findings suggest interdisciplinary competence in the AI era is best understood as a dynamic interplay between human critical agency and AI affordances. The validated ICAIL scale provides a valuable tool for educators and researchers to assess learning outcomes, inform the design of AI-enhanced pedagogies, and foster ethically responsible, adaptive learners prepared for complex socio-technical challenges. It advances both measurement methodology and the theoretical understanding of interdisciplinary learning in the age of AI.
Keywords:
Interdisciplinary competence
AI-enabled learning
Scale validation
Educational technology
Human-AI collaboration