厘清教育领域AI素养的模糊定义，提出三类认知框架与三大视角。
AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review
- 梳理2020年以来124项研究，归纳出功能型、批判型与间接受益型三种素养模式。
- 发现当前对AI素养的理解涵盖从幼儿园到大学的广泛场景，但缺乏统一标准。
- 建议用更精准术语区分不同层次的AI素养目标，适合教育政策制定者参考。
尽管生成式AI使人工智能素养成为教育领域的热点话题，但其定义仍不清晰。学界与实践者对如何讨论和设计相关干预措施缺乏共识，该术语被用于描述从训练本科生有效使用ChatGPT，到让幼儿园儿童与社交机器人互动等广泛活动。本文采用整合性综述方法，分析2020年以来发表的实证与理论研究。通过对124项研究的综合，识别出三种概念化方式：功能性、批判性与间接有益性；以及三种看待AI的视角：技术细节、工具应用与社会文化背景，构建了一个反映实践中多样化路径的框架。该框架揭示了在讨论中需要更专业的术语，并指出了部分素养目标的研究空白。
原文摘要 · Abstract (English)
Even though AI literacy has emerged as a prominent education topic in the wake of generative AI, its definition remains vague. There is little consensus among researchers and practitioners on how to discuss and design AI literacy interventions. The term has been used to describe both learning activities that train undergraduate students to use ChatGPT effectively and having kindergarten children interact with social robots. This paper applies an integrative review method to examine empirical and theoretical AI literacy studies published since 2020. In synthesizing the 124 reviewed studies, three ways to conceptualize literacy-functional, critical, and indirectly beneficial-and three perspectives on AI-technical detail, tool, and sociocultural-were identified, forming a framework that reflects the spectrum of how AI literacy is approached in practice. The framework highlights the need for more specialized terms within AI literacy discourse and indicates research gaps in certain AI literacy objectives.
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