Teachers’ Digital Pedagogical Readiness for AI-Supported Teaching: Preliminary Internal-Structure Evidence for a Five-Dimensional Model

Artificial intelligence (AI) is becoming part of everyday teaching, yet its educational value depends on teachers’ capacity to use AI pedagogically, critically, and ethically. This quantitative cross-sectional study examined preliminary internal-structure evidence for a provisional teacher-readiness item set and compared selected background groups. Survey data from 538 teachers were analysed through exploratory dimension reduction, confirmatory measurement modelling, independent-samples t tests, and one-way ANOVAs. Supplementary analyses evaluated reliability, convergent and discriminant evidence, measurement invariance, oblique factor patterns, and competing measurement models. The retained 31-item model was interpreted as five provisional dimensions: AI-supported instructional design and personalization, collaborative online pedagogy and AI-supported assessment, innovativeness and openness to educational technology, evaluative digital competence, technology confidence, and professional influence, and ethical, reflective, and practical AI pedagogy. The original correlated five-factor model fit was good, but supplementary comparisons also supported a strong general readiness factor. Standardized CFA loadings ranged from .623 to .840. Readiness did not differ by gender or subject specialization; it was higher among teachers with prior AI-related learning and varied by school context and career stage. However, school-type, subject, and experience comparisons require caution because measurement equivalence was not established for those groupings and several tests were marginal after multiplicity adjustment. The findings provide preliminary evidence of a broad readiness profile with related subdomains rather than validation of a fully established instrument.