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RAIL-Ed is an integrative, developmental, and dialectical framework for generative AI literacy in K-12 teacher education, built from a systematic review of 67 studies and specifying six interdependent pillars with a three-level maturity rubric.

Shahin Hossain, Sima Ahmadi, Leqi Li, Idowu David Awoyemi, Wei Huang, Chenxi Zhou, Jujia Li, Samaa Haniya, Shapla Khanam, Tasbirun Mashreka Subaha โ€” arXiv (cs.CY / cs.HC) preprint, 2026 (UMBC, Kent State, Penn State, University of Alabama, etc.).

Synthesis

Developed through a systematic review and qualitative framework analysis of 67 studies (2023-2025), grounded in critical, pragmatist, sociocultural, and human-centered traditions (Freire, Dewey, Vygotsky, Shneiderman).

Specifies six interdependent pillars: Technical Fluency, Critical Evaluation, Human-AI Collaboration, Contextual Awareness, Ethical Reasoning, and Empowered Agency โ€” the absence of any pillar produces a characteristic pedagogical failure.

Developmental: a three-level rubric (Emerging, Competent, Advanced) describes how each pillar matures across the K-12 teacher-preparation continuum.

Dialectical: the same generative affordance can deepen or displace learning depending on teacher literacy, making literacy cultivation โ€” not tool adoption โ€” the object of design.

Aligned with the UNESCO AI Competency Framework for Teachers and the OECD/European Commission AILit Framework; advances falsifiable propositions for empirical validation.

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  • Citation

    Shahin Hossain, Sima Ahmadi, Leqi Li, Idowu David Awoyemi, Wei Huang, Chenxi Zhou, Jujia Li, Samaa Haniya, Shapla Khanam, Tasbirun Mashreka Subaha (2026). Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Education. arXiv:2608.01705. arXiv (cs.CY / cs.HC) preprint.