Agentivism: a learning theory for the age of artificial intelligence

2026-04-09

AI Summary

Existing learning theories struggle to explain how humans achieve durable competence growth when extensively supported by generative and agentive AI, as task success does not necessarily indicate genuine learning. This work proposes “Agentivism,” a novel learning theory that defines authentic learning in human-AI collaboration through selective delegation to AI, cognitive monitoring and verification, reconstructive internalization, and transfer scaffolded by gradual support reduction. Agentivism is the first framework to systematically integrate insights from cognitive science, constructivism, and AI agent models, explicitly distinguishing between AI-augmented performance and actual human capability development. By addressing a critical gap in traditional learning theory, it offers a new paradigm for understanding learning mechanisms, informing educational practices, and guiding the design and evaluation of intelligent instructional tools in the AI era.

Abstract

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.

Problem

• AI-assisted learning
• learning theory
• human-AI interaction
• cognitive delegation
• durable capability

Innovation

• Agentivism
• human-AI interaction
• selective delegation
• epistemic monitoring
• reconstructive internalization