VALIDATING THE ADEM SCALE: A SECOND-ORDER MEASURE OF STUDENTS’ AI DISPOSITION AND ADAPTATION IN RELATION TO AFFECTIVE AND IDENTITY OUTCOMES
DOI:
https://doi.org/10.35631/IJMOE.831018Keywords:
AI Adaptability, AI-Supported Learning, Ethical AI Engagement, Higher Education, Intellectual ReadinessAbstract
The rapid integration of artificial intelligence (AI) into university learning has created a need for a comprehensive measure that considers not only students’ ability to use AI but also their intellectual readiness, emotional regulation, ethical values, and identity as learners. This cross-sectional study assessed the psychometric quality of the 70-item AI Disposition and Adaptation Model (ADEM) and examined how AI Adaptability, Epistemological Awareness, Resource Integration, and Intellectual Readiness relate to students’ overall adaptation to AI-supported learning. ADEM was represented by three interconnected dimensions: Emotional Balance, Axiological Alignment, and Ontological Perspective. Survey data were collected from 280 higher-education students. The findings demonstrated strong reliability and convergent validity across the measured dimensions, while also identifying some overlap between closely related constructs that should be considered when interpreting the instrument. AI Adaptability, Epistemological Awareness, Resource Integration, and Intellectual Readiness were significantly associated with ADEM. Intellectual Readiness also partially explained how students’ adaptability, critical evaluation of AI-generated knowledge, and integration of AI with authoritative academic resources contributed to their broader adaptation to AI-supported learning. This study introduces a multidimensional framework that extends beyond technical competence by incorporating emotional well-being, responsible knowledge evaluation, ethical conduct, and learner agency. The ADEM instrument offers a practical basis for assessing students’ strengths and development needs, informing AI-literacy curricula, designing targeted educational interventions, and evaluating whether AI-supported learning initiatives promote capable, critical, balanced, and ethically responsible engagement with AI.
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