Development and validation of a forward-looking metacognitive AI readiness scale for Jordanian teachers

With the rapid expansion of artificial intelligence (AI) in education, valid tools are needed to assess teachers' readiness for anticipatory, adaptive, and reflective engagement with AI-supported instruction. This study developed and evaluated the Forward-Looking Metacognitive-AI Readiness Scale (FLMAIRS) among secondary school teachers in Jordan. An exploratory mixed-methods design was used. Items were generated through a literature review and semi-structured interviews with 22 teachers, followed by expert review, content and face validity assessment, and cognitive interviewing. The field-test version was administered online to 640 teachers and randomly split for exploratory factor analysis (EFA; n = 320) and confirmatory factor analysis (CFA; n = 320). EFA supported a five-factor, 45-item structure: AI Foresight Planning, Adaptive AI-Oriented Instructional Regulation, Algorithmic Anticipatory Reasoning, Human-AI Instructional Synergy, and Long-Term AI-Reflective Practice, explaining 58.29% of the variance. CFA supported acceptable first- and second-order model fit. Reliability was high across dimensions (Cronbach's α = 0.897-0.931; McDonald's ω = 0.900-0.932), and test-retest stability was good (ICC = 0.752-0.851). Convergent and discriminant validity were supported. FLMAIRS provides a context-sensitive instrument for assessing teachers' forward-looking metacognitive readiness for AI-integrated education.