# Agentivism: a learning theory for the age of artificial intelligence

arXiv: https://arxiv.org/abs/2604.07813 · HTML: https://mlsanity.com/paper/2604.07813/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 95 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [ELEPHANT: Measuring and understanding social sycophancy in LLMs](https://mlsanity.com/paper/2505.13995/) (2025)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial](https://mlsanity.com/paper/2403.14380/) (2024)
- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance](https://mlsanity.com/paper/2412.09315/) (2024)
- [Machine Culture](https://mlsanity.com/paper/2311.11388/) (2023)
- [The Homogenizing Effect of Large Language Models on Human Expression and Thought](https://mlsanity.com/paper/2508.01491/) (2025)
- [Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction](https://mlsanity.com/paper/2409.16708/) (2024)
- [How Far Are We? The Triumphs and Trials of Generative AI in Learning Software Engineering](https://mlsanity.com/paper/2312.11719/) (2023)
- [Towards Reliable Generative AI-Driven Scaffolding: Reducing Hallucinations and Enhancing Quality in Self-Regulated Learning Support](https://mlsanity.com/paper/2508.05929/) (2025)
- [Bridging Learnersourcing and AI: Exploring the Dynamics of Student-AI Collaborative Feedback Generation](https://mlsanity.com/paper/2311.12148/) (2023)
- [When Machines Join the Moral Circle: The Persona Effect of Generative AI Agents in Collaborative Reasoning](https://mlsanity.com/paper/2511.01205/) (2025)

## Abstract (arXiv)

Learning theories have historically changed when the conditions of learning evolved. Generative and agentic AI create a new condition by allowing learners to delegate explanation, writing, problem solving, and other cognitive work to systems that can generate, recommend, and sometimes act on the learner's behalf. This creates a fundamental challenge for learning theory: successful performance can no longer be assumed to indicate learning. Learners may complete tasks effectively with AI support while developing less understanding, weaker judgment, and limited transferable capability. We argue that this problem is not fully captured by existing learning theories. Behaviourism, cognitivism, constructivism, and connectivism remain important, but they do not directly explain when AI-assisted performance becomes durable human capability. We propose Agentivism, a learning theory for human-AI interaction. Agentivism defines learning as durable growth in human capability through selective delegation to AI, epistemic monitoring and verification of AI contributions, reconstructive internalization of AI-assisted outputs, and transfer under reduced support. The importance of Agentivism lies in explaining how learning remains possible when intelligent delegation is easy and human-AI interaction is becoming a persistent and expanding part of human learning.