cs.AIApr 9, 2026

Agentivism: a learning theory for the age of artificial intelligence

Authors: Lixiang Yan, Dragan Gašević

Organizations: School of Education, Tsinghua University. · Faculty of Education and School of Computing & Data Science, The University of Hong Kong. · Faculty of Information Technology, Monash University.

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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.

Figures & tables

TheoryCan explainFalls short
BehaviourismWhy AI use can become reinforcing through immediate feedback, fluency, speed, effort reduction, and repetition Watson (1913) ; Skinner (1938) ; Ngu et al. (2025) ; Pan et al. (2025) .Whether repeated AI-supported success reflects durable learning or merely reinforced dependence; why slowing users down through justification can reduce over-reliance Bučinca et al. (2021) .
CognitivismHow AI changes cognitive load, memory demands, schema construction, retrieval, and offloading during learning Miller (1956) ; Atkinson and Shiffrin (1968) ; Stadler et al. (2024) ; Bastani et al. (2025) ; Liu et al. (2025) .When offloading supports learning versus substitutes for it; how AI-assisted performance becomes retained, transferable human competence.
ConstructivismWhy dialogue, interpretation, participation, and meaning making remain central when learners interact with AI conversationally Dewey (1938) ; Piaget (1952) ; Vygotsky (1978) ; Kim et al. (2026) ; Jin et al. (2025) ; Xing et al. (2026) .Whether AI-generated guidance is epistemically trustworthy; interaction alone does not guarantee justified learning Salvi et al. (2025) .
ConnectivismWhy learning depends on distributed networks of people, tools, and information resources Siemens (2005) .What changes when network nodes become generative, persuasive, and partially agentic, shaping representation, priorities, and action in real time Wang et al. (2023) ; Tan et al. (2026) .
Table 1: What classical learning theories can explain, and where they fall short in the age of generative and agentic AI
Figure 1: Conceptual model of Agentivism as a learning theory for human-AI interaction. Agentivism explains learning as durable growth in human capability under conditions where generative or agentic AI contributes directly to task completion. The figure presents four linked mechanisms. Delegated agency describes how responsibility for task execution is distributed between learner and AI. Epistemic monitoring and verification captures how learners evaluate AI outputs for truthfulness, relevance, adequacy, and fit to task demands. Reconstructive internalization explains how AI-assisted performance is transformed into knowledge and skill that the learner can explain and use independently. Transfer under reduced support indicates that learning is evidenced when the learner can adapt and perform with less reliance on AI. Together these mechanisms distinguish assisted performance from learning and specify when human capability is retained beyond the immediate interaction.
Figure 2: Empirical propositions derived from the mechanisms of Agentivism. This figure maps six empirically testable propositions (P1–P6) onto the four core mechanisms of Agentivism, illustrating how predictions about learning outcomes arise from specific process components.

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