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arxiv: 2604.07813 · v1 · submitted 2026-04-09 · 💻 cs.AI · cs.HC

Recognition: 2 theorem links

· Lean Theorem

Agentivism: a learning theory for the age of artificial intelligence

Lixiang Yan , Dragan Ga\v{s}evi\'c
Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:56 UTC · model grok-4.3

classification 💻 cs.AI cs.HC
keywords Agentivismlearning theoryhuman-AI interactionAI-assisted learningdurable capabilityselective delegationepistemic monitoringinternalization
0
0 comments X

The pith

Agentivism defines learning in the AI era as durable human capability built through selective delegation, monitoring, internalization, and unsupported transfer.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing theories of learning do not explain how performance achieved with AI support becomes independent human skill once the AI is removed. Learners can finish tasks effectively while AI handles the cognitive work, yet show little lasting understanding or judgment. The paper therefore proposes Agentivism as a dedicated theory for human-AI interaction. It states that genuine learning occurs only when people choose what to delegate, check the AI's output, rebuild it in their own terms, and later demonstrate the same capability without help. This distinction matters because AI tools are now a routine part of study and work, so instruction must deliberately cultivate the four processes rather than assume performance equals learning.

Core claim

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 theory addresses the fact that successful performance with AI no longer reliably signals learning, since learners may complete work effectively while developing weaker understanding and limited transferable skill.

What carries the argument

The four-process definition of learning within Agentivism: selective delegation to AI, epistemic monitoring and verification, reconstructive internalization, and transfer under reduced support.

If this is right

  • Instructional designs must include explicit practice in verifying and reconstructing AI contributions rather than relying on final output alone.
  • Assessment of learning requires phases of reduced or removed AI support to confirm that capability has transferred.
  • Curricula should teach learners criteria for deciding which tasks to delegate and which to keep under human control.
  • Educational tools need features that prompt epistemic monitoring and internalization instead of simply accelerating task completion.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same four processes could be examined in non-educational settings such as professional workflows where AI assists with reports or code.
  • Interfaces could be redesigned to make monitoring and reconstruction easier, potentially increasing the rate at which AI assistance converts into independent skill.
  • Long-term studies might track whether repeated use of the four processes changes how learners approach new problems even when no AI is present.

Load-bearing premise

Existing learning theories cannot directly explain the conditions under which AI-assisted performance produces lasting human capability once support is withdrawn.

What would settle it

A controlled study that measures retention of problem-solving skill after AI removal in two groups, one trained to monitor and internalize AI outputs and one allowed to delegate without monitoring.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes Agentivism as a new learning theory tailored to human-AI interaction. It argues that established theories (behaviourism, cognitivism, constructivism, connectivism) do not directly explain the conditions under which AI-assisted task performance produces durable growth in human capability. Agentivism defines learning as such growth achieved via four processes: selective delegation to AI, epistemic monitoring and verification of AI contributions, reconstructive internalization of AI-assisted outputs, and transfer under reduced support.

Significance. If the proposed framework can be shown to identify mechanisms not already covered by extensions of prior theories, it could usefully guide the design of AI tools and educational interventions that prioritize capability retention over short-term performance. As a purely conceptual contribution without empirical tests, formal derivations, or falsifiable predictions, its significance remains prospective and depends on subsequent validation.

major comments (2)
  1. Abstract: The central motivation—that behaviourism, cognitivism, constructivism, and connectivism 'do not directly explain when AI-assisted performance becomes durable human capability'—is asserted without a comparative gap analysis or counterexamples. The manuscript does not demonstrate why, for instance, constructivist scaffolding and internalization (Vygotsky) cannot already subsume epistemic monitoring and reconstructive internalization when AI functions as an external tool, leaving the necessity of new primitives unsupported.
  2. Abstract and proposed definition: The four processes are presented as jointly sufficient for durable growth, yet no mechanism, interaction rules, or boundary conditions are supplied showing how selective delegation plus monitoring reliably produces internalization and transfer rather than performance-only outcomes. This renders the definitional claim load-bearing but ungrounded.
minor comments (2)
  1. The manuscript introduces 'Agentivism' without situating it against related concepts already in the AI-education literature (e.g., agentic learning or tool-mediated cognition), which would clarify novelty.
  2. No section outlines testable predictions or operational measures for the four processes, which would strengthen the proposal even as a conceptual framework.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review of our manuscript on Agentivism. We address each major comment below, providing clarification on the conceptual scope of the work while indicating revisions where they strengthen the presentation without altering its foundational claims.

read point-by-point responses

  1. Referee: Abstract: The central motivation—that behaviourism, cognitivism, constructivism, and connectivism 'do not directly explain when AI-assisted performance becomes durable human capability'—is asserted without a comparative gap analysis or counterexamples. The manuscript does not demonstrate why, for instance, constructivist scaffolding and internalization (Vygotsky) cannot already subsume epistemic monitoring and reconstructive internalization when AI functions as an external tool, leaving the necessity of new primitives unsupported.

    Authors: We acknowledge the value of making the comparative gap more explicit. The manuscript motivates the proposal by noting that generative AI enables complete cognitive delegation in ways that differ from prior tools, such that performance can occur without the learner engaging in the reconstructive or monitoring processes that existing theories presuppose. To address the referee's point directly, we will expand the introduction with a short comparative subsection that contrasts Vygotsky-style scaffolding (where the more knowledgeable other remains human and the learner retains active construction) with AI delegation scenarios where the system can generate complete solutions, thereby illustrating why the four Agentivism processes are not automatically subsumed. This revision will supply the requested counterexamples while preserving the paper's conceptual focus. revision: yes

  2. Referee: Abstract and proposed definition: The four processes are presented as jointly sufficient for durable growth, yet no mechanism, interaction rules, or boundary conditions are supplied showing how selective delegation plus monitoring reliably produces internalization and transfer rather than performance-only outcomes. This renders the definitional claim load-bearing but ungrounded.

    Authors: The manuscript offers Agentivism as a definitional framework that identifies the conditions under which AI-assisted performance yields durable capability, rather than as a mechanistic model with formal interaction rules or boundary conditions. The four processes are proposed as jointly necessary on logical grounds: without selective delegation, monitoring, internalization, and transfer, capability growth is not assured when AI performs substantial cognitive work. Detailed mechanisms and falsifiable predictions are beyond the scope of this initial conceptual paper and are explicitly flagged as directions for subsequent empirical research. We will revise the discussion section to state this scope limitation more clearly and to outline example research questions that could test the proposed processes, thereby grounding the definition without overclaiming sufficiency. revision: partial

Circularity Check

0 steps flagged

No circularity: Agentivism is an independent definitional proposal

full rationale

The paper advances a new learning theory by explicitly defining learning as durable capability growth via four mechanisms (selective delegation, epistemic monitoring, reconstructive internalization, transfer under reduced support). No equations, fitted parameters, or predictions appear; the claim that prior theories (behaviourism, cognitivism, constructivism, connectivism) do not directly explain AI-assisted durable capability is stated as a premise without reduction to self-citation chains or imported uniqueness theorems. The contribution stands as a conceptual reframing rather than a derivation that collapses to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The proposal rests on two domain assumptions about the limits of prior theories and introduces one new conceptual entity without independent evidence.

axioms (2)
  • domain assumption Successful performance can no longer be assumed to indicate learning when AI systems can generate, recommend, and act on the learner's behalf.
    Stated as the fundamental challenge created by generative and agentic AI.
  • domain assumption Behaviourism, cognitivism, constructivism, and connectivism do not directly explain when AI-assisted performance becomes durable human capability.
    Explicitly argued as the motivation for proposing a new theory.
invented entities (1)
  • Agentivism no independent evidence
    purpose: A learning theory framework for human-AI interaction
    Newly proposed theory whose four processes are defined without prior empirical support or derivation from data.

pith-pipeline@v0.9.0 · 5499 in / 1443 out tokens · 31627 ms · 2026-05-10T17:56:33.215897+00:00 · methodology

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Reference graph

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