Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Education

📄 arXiv: 2608.01705v1 📥 PDF

作者: Shahin Hossain, Sima Ahmadi, Leqi Li, Idowu David Awoyemi, Wei Huang, Chenxi Zhou, Jujia Li, Samaa Haniya, Shapla Khanam, Tasbirun Mashreka Subaha

分类: cs.CY, cs.AI, cs.ET, cs.HC

发布日期: 2026-08-03

备注: 20 pages, 2 figures, 7 tables

💡 一句话要点

提出RAIL-Ed框架以解决K-12教师生成AI素养不足问题

🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 生成AI 教师教育 AI素养 伦理教育 公平性 人机协作 教育政策

📋 核心要点

  1. 现有的AI素养框架未能跟上生成AI技术的快速发展，导致教师在技术、教学和伦理方面的准备不足。
  2. 本文提出的RAIL-Ed框架通过六个相互依赖的支柱，强调技术流畅性、批判性评估、人机协作等方面的综合素养。
  3. RAIL-Ed框架为教师教育提供了一个三层次的成熟度标准，帮助教师在K-12教育中逐步提升生成AI素养。

📝 摘要（中文）

生成人工智能（GenAI）迅速进入课堂，但教师在使用方面的准备不足，导致生成AI素养滞后。现有的AI素养框架未能充分应对大语言模型的广泛应用，且将伦理视为独立能力而非核心承诺。本文提出的负责任AI素养教育框架（RAIL-Ed），基于67项研究的系统回顾和定性框架分析，明确了六个相互依赖的支柱，并强调伦理、公平和能力的重要性，为教师教育和政策设计提供了理论基础。

🔬 方法详解

问题定义：本文旨在解决教师在生成AI技术应用中的素养不足，现有框架未能有效整合伦理和公平等核心要素。

核心思路：RAIL-Ed框架通过六个支柱构建综合素养，强调伦理和能力作为教育设计的核心，旨在提升教师的生成AI素养。

技术框架：RAIL-Ed框架包含六个支柱：技术流畅性、批判性评估、人机协作、情境意识、伦理推理和赋权能力，形成一个相互依赖的整体。

关键创新：RAIL-Ed框架的创新在于将伦理、公平和能力视为教育设计的核心，而非附加原则，推动教师素养的全面提升。

关键设计：框架采用三层次的成熟度标准（初步、合格、先进），为教师在不同阶段的生成AI素养发展提供明确指导。

🖼️ 关键图片

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📊 实验亮点

RAIL-Ed框架通过系统分析67项研究，提出了六个支柱，强调伦理和能力的重要性，为教师教育提供了理论基础，推动了教师生成AI素养的提升，具有显著的实践价值。

🎯 应用场景

RAIL-Ed框架可广泛应用于K-12教师教育和培训，帮助教师有效整合生成AI技术于课堂教学中，提高教育质量和学生学习体验。未来，该框架还可能影响教育政策的制定，推动教育公平和伦理的落实。

📄 摘要（原文）

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.