Development, Network Analysis, and Validation of the Ethical–Spiritual Algorithmic Trust Calibration Scale (ES-ATCS): Exploring Teachers’ Ethical and Spiritual Trust in AI Integration Within Jordanian Secondary Education

The purpose of this study is to design and evaluate the psychometric properties of the Ethical–Spiritual Algorithmic Trust Calibration Scale (ES-ATCS) among teachers. The primary aim was to develop a reliable and valid instrument to understand how teachers navigate trust, ethical accountability, and spiritual coherence when engaging with AI-driven educational technologies. The study was conducted in two main phases. Phase 1 comprised item generation, 12 specialist expert reviews (Lawshe CVR cutoff = 0.56), and pilot testing (n = 35 teachers), which reduced the item pool through CVR/I-CVI filtering and impact-score analyses. Phase 2 involved a cross-sectional sample of 666 teachers, which was randomly split into two halves for EFA and CFA. EFA and exploratory graph analysis suggested a coherent six-factor structure accounting for 63.20% of total variance, with the Spiritual Coherence Perception factor explaining 12.26% of the variance. Iterative CFA supported a final 48-item first- and second-order six-factor model with acceptable fit (RMSEA <.08; CFI, TLI >.90; SRMR <.08) and standardized loadings >.40. Measurement invariance was acceptable across gender and teaching experience. Reliability (Cronbach’s α.898–.953, McDonald’s ω.848–.953, CR.898–.954) and stability were strong: ICCs ranged from.755 to.853. Convergent (AVE.501–.940) and discriminant validity (Fornell–Larcker) were acceptable. Network EGA identified 43 nodes and 209 edges with six communities; centrality indices identified salient items. The 48-item ES-ATCS is culturally sensitive and psychometrically sound for measuring teachers’ ethical–spiritual trust in AI.