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

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.