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<p>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; <i>n</i> = 320) and confirmatory factor analysis (CFA; <i>n</i> = 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月26日

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  • Haitham Salem Baibers,
  • Mohammad Nayef Ayasrah

摘要

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.