Date
Publisher
arXiv
Generative artificial intelligence (GenAI) has entered classrooms faster than teachers have been prepared to use it well, producing a GenAI literacy lag in which technological diffusion outpaces educators' conceptual, pedagogical, and ethical readiness. Established AI literacy frameworks predate the widespread adoption of large language models and, while acknowledging ethics, position it as a discrete competency rather than a constitutive commitment, with equity and agency as supplementary design principles. Recent GenAI-specific efforts address isolated features but remain fragmented. We introduce the Responsible AI Literacy in Education (RAIL-Ed) framework, 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). RAIL-Ed specifies six interdependent pillars: Technical Fluency, Critical Evaluation, Human-AI Collaboration, Contextual Awareness, Ethical Reasoning, and Empowered Agency, marked by three commitments. It is integrative: the absence of any pillar produces a characteristic pedagogical failure. It is developmental: a three-level rubric (Emerging, Competent, Advanced) specifies how each pillar matures across the K-12 teacher-preparation continuum. It is dialectical: the same generative affordance can deepen or displace learning depending on the literacy a teacher brings to it, making the cultivation of that literacy, not the adoption of the tool, the object of design. By treating ethics, equity, and agency as constitutive, RAIL-Ed offers a theoretically grounded basis for curriculum design, teacher education, and policy, aligned with the UNESCO AI Competency Framework for Teachers and the OECD/European Commission AILit Framework. The framework is conceptual, advancing falsifiable propositions for empirical validation.
Study at a glance
- Question: What generative AI literacy framework can prepare K-12 teachers to use AI effectively and ethically?
- Setting and data: 67 studies published between 2023 and 2025
- Method: Systematic literature review and qualitative framework analysis
Why it matters
Generative AI has entered classrooms faster than teachers have been prepared to use it well, creating a literacy lag. Established frameworks predate large language models, while recent efforts address isolated features and remain fragmented.
Key findings
- Across 67 reviewed studies (2023–2025), classroom adoption of generative AI has outpaced K-12 educators' conceptual, pedagogical, and ethical readiness.
- The same generative AI tool can either deepen or displace learning depending on the literacy an educator brings to its use.
- Teacher AI literacy requires six interdependent competencies, where the absence of any single pillar results in a characteristic pedagogical failure.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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