Development and validation of moral ascendancy and dependency in AI integration (MAD-AI) scale for teachers

Abstract

Integrating artificial intelligence into teaching practices has become a prominent topic in recent literature, raising concerns about how teachers' moral ascendancy and dependency can be attributed and measured in a behavioral context. This study addresses this gap by developing and validating the teachers' perceived moral ascendancy and dependency on the AI integration (MAD-AI) scale. The methodological process involved the following key steps: initially identifying the scale's dimensionality and creating draft items through a literature review; conducting validation with four experts; revising items based on expert feedback; holding two focus group discussions with six preservice and four in-service teachers; refining dimensions and items based on these discussions; and finally, establishing the psychometric properties of the scale through exploratory factor analysis and confirmatory factor analysis with 383 survey responses from 224 in-service and 159 preservice teachers in the Philippines. The final 30-item MAD-AI scale for teachers includes two dimensions for moral ascendancy: (a) ethical transparency and accountability (7 items) and (b) professional integrity (8 items), as well as two dimensions for dependency: (a) institutional support (8 items) and (b) educator preparedness (7 items). Results demonstrated acceptable fit measures, strong reliability, convergent validity, and discriminant validity, supporting the structural soundness of the scale.