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

arXiv: https://arxiv.org/abs/2606.12441 · HTML: https://mlsanity.com/paper/2606.12441/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 66 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 6 of 6)

- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance](https://mlsanity.com/paper/2412.09315/) (2024)
- [Evaluating Human-AI Collaboration: A Review and Methodological Framework](https://mlsanity.com/paper/2407.19098/) (2024)
- [Generative AI Literacy: Twelve Defining Competencies](https://mlsanity.com/paper/2412.12107/) (2024)
- [Effective Generative AI: The Human-Algorithm Centaur](https://mlsanity.com/paper/2406.10942/) (2024)
- [Can generative AI and ChatGPT outperform humans on cognitive-demanding problem-solving tasks in science?](https://mlsanity.com/paper/2401.15081/) (2024)

## Abstract (arXiv)

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.