提出新学习理论Generativism，应对生成式AI时代的教育挑战
Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence
- 人与AI协同迭代构建知识，形成新型学习模式
- 提出四维框架：认知协作、分布式智能、生成素养、自适应元认知
- 适合教育设计者、教师及研究生成式AI教学应用者
行为主义、认知主义、建构主义和联结主义四种主流学习理论在生成式人工智能广泛应用于教育场景时暴露出概念局限。这些理论形成于生成式AI出现之前，难以应对能够生成、合成和推理知识的AI系统带来的变革。本文批判性审视各理论，识别其被生成式AI功能挑战的核心假设。基于分布式认知、延伸心智、人机协作、AI素养、认知卸载和元认知等研究，提出面向生成式AI时代的学习理论——Generativism。该理论认为，学习正日益通过人类学习者与AI系统之间的迭代式知识共建实现。框架包含四个核心构念（认知伙伴关系、分布式智能、生成素养、自适应元认知）及其相互关系的四条原则，为生成式AI深度融入认知的教学设计、学习、评估与专家能力发展提供了理论基础。
原文摘要 · Abstract (English)
The four dominant learning theories of behaviorism, cognitivism, constructivism, and connectivism show significant conceptual limitations as generative artificial intelligence (AI) proliferates in educational settings. These frameworks were formulated before the emergence of AI systems capable of generating, synthesizing, and reasoning about knowledge. This article critically examines each learning theory and identifies assumptions challenged by the affordances of generative AI. Drawing on research in distributed cognition, extended mind, human-AI collaboration, AI literacy, cognitive offloading, and metacognition, the article proposes Generativism as a learning theory for the generative AI age. Generativism posits that learning increasingly occurs through the iterative co-construction of knowledge between human learners and AI systems. The proposed framework is organized around four constructs (epistemic partnership, distributed agency, generative literacy, and adaptive metacognition) and four principles specifying the relations among them. The framework offers a foundation for rethinking instructional design, learning, assessment, and expertise development in contexts where generative AI plays an integral role in cognition.
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