Cognitive, Spiritual, and Algorithmic Responsiveness in Teaching Scale (C-SARTS): Exploring Teachers' Cognitive, Ethical, and Spiritual Responsiveness in AI-Supported Secondary Education in Jordan.

Résumé

This study developed and psychometrically validated the Cognitive-Spiritual Algorithmic Responsiveness in Teaching Scale (C-SARTS) for AI-supported secondary education in Jordan. Using a sequential mixed-methods design, we generated and refined items through qualitative interviews and literature review, then administered the scale to 552 teachers. Exploratory factor analysis suggested a coherent four-factor solution-integrative multidimensional responsiveness, cognitive responsiveness in live pedagogy, spiritual responsiveness in teaching encounters, and algorithmic responsiveness in instructional design-which was subsequently confirmed by confirmatory factor analysis with acceptable fit indices. Reliability was strong across dimensions (α/ω/CR at or above conventional thresholds). Convergent and discriminant validity were largely satisfactory; one construct showed AVE slightly below 50 but met composite reliability criteria, indicating conservative yet acceptable convergence. Measurement invariance held across gender. Network-based exploratory graph analysis with bootstrap replications supported a stable four-dimensional structure, and a random-forest check highlighted the integrative dimension and items that combine AI-readable design with attention to students' psychosocial context as most influential. C-SARTS offers a concise, multidimensional measure of how teachers align cognitive, ethical-spiritual, and algorithmic considerations in AI-rich classrooms, supporting applications in educational research, teacher professional development, and ethically grounded technology integration in Jordanian secondary schools.