Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence

2026-05-18

arXiv.org

AI Summary

本文针对现有学习理论在生成式AI环境下的局限性，提出了一种新的学习理论——生成主义，通过人与AI系统的知识共构来促进学习。

Abstract

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 generative AI's affordances. 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 principles: epistemic partnership, distributed agency, generative literacy, and adaptive metacognition. 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.

Problem

• generative artificial intelligence
• learning theories
• conceptual limitations
• educational settings

Innovation

• Generativism
• generative AI
• co-construction of knowledge
• distributed cognition
• metacognition