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

Li & Zheng (2026).

Li & Zheng argue that the four dominant learning theories — behaviorism, cognitivism, constructivism, and connectivism — show significant conceptual limitations as Generative AI proliferates in educational settings. They propose Generativism, a new learning theory for the generative AI age, which posits that learning increasingly occurs through the iterative co-construction of knowledge between human learners and AI systems.

The theory is built on four core principles:

1. Epistemic Partnership — Humans and AI collaboratively construct knowledge through iterative dialogue and co-reasoning, extending Human AI Collaboration research.

2. Distributed Agency — Learning is a shared process where both human and AI exert influence over knowledge outcomes, building on Cognitive Offloading LLM Synthesis Writing and Cognitive Shift AI Education.

3. Generative Literacy — Learners must develop skills to critically interpret, evaluate, and guide AI-generated content, complementing AI Literacy frameworks.

4. Adaptive Metacognition — Learners monitor and regulate their own cognition and the AI's contributions during collaborative learning, extending Scaffolding and Self Regulated Learning.

Generativism has profound implications for instructional design, learning and assessment, and expertise development in contexts where generative AI plays an integral role in cognition. The framework challenges existing educational AI principles and offers a foundation for rethinking how large language models reshape the fundamental nature of learning itself.

Connected Concepts

Connected Articles

Citation

Li, S., & Zheng, J. (2026). Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence. arXiv:2606.12441.