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REVIEW 3 major objections 6 minor 11 references

From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learning

T0 review · 3 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper proposes the APCP framework—four levels of escalating AI agency in collaborative learning—and argues that AI can be a highly effective functional collaborator even though it cannot be an authentic one.

desk verdict A clear, useful synthesis for the AIED community, but the four-level agency ladder rests on an unargued single-dimension assumption and needs operational criteria before it can do the evaluative work claimed. read the letter →

arxiv 2508.14825 v1 pith:RXZ4GZCK submitted 2025-08-20 cs.HC cs.AI

Lixiang Yan This is my paper · ORCID
classification cs.HCcs.AI
keywords AgenticAIHuman-AICollaborationCollaborativeLearningComputer-SupportedinEducationAPCPframeworkFunctionalagency
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper is trying to give the field a way to talk about a genuinely new situation: AI that does not merely respond to a learner but acts on its own, with its own goals, inside a learning group. It proposes the APCP framework, which sorts human-AI collaborative learning into four levels of escalating AI agency—adaptive instrument, proactive assistant, co-learner, and peer collaborator—and grounds these levels in sociocultural learning theory. The payoff would be a shared vocabulary for designing, describing, and evaluating agentic AI in education, replacing the simplistic tool-versus-partner debate. The paper's second claim is philosophical: because AI lacks consciousness, subjective experience, and shared intentionality, it cannot be an authentic collaborator, but it can be a highly effective functional collaborator if designed to perform the observable moves of good collaboration.

What carries the argument

The central object is the APCP framework, a four-level taxonomy of AI agency—Adaptive Instrument, Proactive Assistant, Co-Learner, Peer Collaborator—that describes the shifting allocation of roles, initiative, and control between human and AI in collaborative learning. Its companion distinction is functional versus authentic collaboration. The framework does the argument's work by giving designers and researchers a structured vocabulary for placing any human-AI learning interaction on an agency scale, and by separating what an AI does that looks like collaboration (observable, trainable, testable) from what an AI is (inaccessible and, the paper argues, impossible for artificial systems).

What would settle it

A transcript-coding study of real human-AI learning dialogues that cannot reliably assign episodes to the four APCP levels, or that finds an AI moving through all four levels within a single session with no change in learner-perceived partnership, would show that the proposed level boundaries are not real distinctions; alternatively, a controlled experiment comparing Level 2, 3, and 4 configurations on the same task that finds no differences in collaboration quality or learning outcomes would undercut the framework's practical utility.

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Extended reading notes

Core claim

The central claim is that agentic AI in collaborative learning is best understood not as a binary choice between tool and partner but as a graded continuum of agency, captured by the APCP framework. Level 1, Adaptive Instrument, mirrors a master-servant relationship: the AI executes explicit commands under full human control. Level 2, Proactive Assistant, gives the AI bounded initiative: it monitors, alerts, and suggests, but the human retains veto power. Level 3, Co-Learner, distributes agency more symmetrically: human and AI share a problem space, divide work, explain and teach one another, and co-construct meaning. Level 4, Peer Collaborator, gives the AI a persistent persona, epistemic s

Load-bearing premise

The load-bearing premise is that a single escalating dimension of AI agency—from instrument to peer—is the dimension that actually organizes meaningful differences in collaborative learning, rather than task type, learner expertise, or social context, and that the four levels are distinct enough to be told apart in practice.

Editorial extensions

If this is right

  • If the APCP framework is accepted, evaluation of educational AI shifts from 'how capable is this AI?' to 'what agency level is it designed for, and is that level appropriate for the learning goal?'
  • Designers should prioritize considerate interaction, transparency, and preservation of meaningful human contribution over optimizing the AI's standalone performance.
  • Educators would take on the role of learning architects, deliberately choosing among the four levels to match the pedagogical objective.
  • Researchers should run comparative studies of discourse and outcomes across levels—especially Level 3 versus Level 4—and longitudinal studies of skill transfer and dependency or over-reliance.
  • Because authentic partnership is unattainable, design and evaluation should focus on functional collaborative behaviors: turn-taking, constructive roles, alternative perspectives, and shared-task outcomes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The four levels could plausibly be operationalized as configurable autonomy parameters—initiative threshold, interruption protocol, persona persistence—in a single adaptive system, letting one AI shift roles by design; the paper does not specify such an implementation.
  • If formalizing collaboration for AI makes collaboration skills explicit and teachable, then deliberately imperfect AI collaborators could serve as practice partners for teaching humans collaboration skills, going beyond the paper's own framing.
  • The functional-versus-authentic distinction may generalize to other human-AI teaming domains, where acceptance of an AI teammate may hinge on functional reliability and preserved human agency rather than on whether the AI 'really' understands.
  • A testable prediction follows from the framework: learning gains and perceived collaborative quality should be sensitive to agency level, but with diminishing or even negative returns at Level 4 for novices if productive friction becomes unproductive frustration; the paper hints at calibration but does not test it.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This conceptual paper argues that existing tool/tutor metaphors are insufficient for understanding agentic AI in collaborative learning. It proposes the APCP framework, which charts four levels of escalating AI agency: Adaptive Instrument, Proactive Assistant, Co-Learner, and Peer Collaborator. Each level is described in terms of roles, responsibilities, interaction dynamics, pedagogical value, design requirements, and illustrative empirical studies. The paper then asks whether an AI can ever be a true collaborator, distinguishes functional collaboration from authentic phenomenological partnership, and argues that designers should aim for the former. It closes with implications for pedagogy, design, and a future research agenda.

Significance. The paper offers a useful conceptual vocabulary and a clear articulation of design options for human-AI collaborative learning. Its grounding in sociocultural theory and CSCL is appropriate, and the distinction between functional and authentic collaboration is a valuable corrective to both uncritical enthusiasm and wholesale dismissal. The level descriptions are readable and concrete, and the research agenda in Section 6.3 includes specific, testable questions. However, the framework's central claim—that the four levels are distinct and ordered along a single dimension of agency—is not operationalized. The selected studies are illustrative rather than confirmatory, and the philosophical argument for the authenticity barrier is asserted more than defended. If the operationalization and dimensionality concerns are addressed, the framework could become a genuinely useful organizing device for the field.

major comments (3)
  1. [Section 4, framework introduction and §4.1–4.4] The central claim that each level is "a distinct configuration of roles, responsibilities, and interaction dynamics" is not operationalized. The text characterizes levels by different features (proactivity, task autonomy, social role, persona, epistemic stance) but gives no criteria for deciding which level a particular system occupies. The examples illustrate the ambiguity: Codellaborator in §4.2 writes substantive code and could be classified as a co-learner (Level 3), while Novobo in §4.3 is a teachable agent with a persistent role and could be classified as a peer collaborator (Level 4) under a broad reading of the §4.4 criteria. Without a decision procedure—e.g., observable indicators, threshold conditions, or a classification checklist—the framework cannot serve its claimed descriptive and evaluative function, and the "four distinct levels" claim is not falsifiable. Section 6.3 ack
  2. [Section 4 intro and Section 3] The framework assumes a single linear "continuum of escalating AI agency." Yet Section 3 defines agency via a perception–reasoning–action–adaptation loop, while the four levels differ in proactivity, task autonomy, social role, persistence of persona, and epistemic stance. The paper does not show that these dimensions co-vary or that agency is the most important organizing variable. A system could be highly autonomous in executing assigned tasks yet entirely reactive in initiating them; such a system would be unclassifiable on the proposed continuum. The authors should either justify unidimensionality or present the levels as prototypical configurations in a multidimensional design space, with an explicit mapping from each level to the relevant dimensions. This is load-bearing for the paper's claim to provide a structured vocabulary for designing and evaluating human-AI collaborative lea
  3. [Section 5.1] The conclusion that AI cannot be an authentic collaborator rests on the "Intersubjectivity Barrier" argument, which cites Searle, Dreyfus, and Yıldız but does not engage with substantive alternative positions such as functionalism, enactivism, or relational accounts of cognition. Because the paper uses this conclusion to set design goals ("the goal should be to build systems that can effectively and reliably perform the functions of a good collaborator"), the argument needs to be more than an appeal to a "widely held" view. At minimum, the paper should acknowledge the contested nature of the consciousness question and state what empirical or conceptual evidence would change its conclusion. This concern does not undermine the descriptive framework itself, but it affects the strength of the philosophical resolution offered in Section 5.
minor comments (6)
  1. [§4.1–4.4] The phrase "Empirical evidence demonstrates" is too strong for studies that were not designed to test the APCP levels. The cited studies are illustrative examples; suggest softening to "illustrates" or "provides initial support" and explicitly noting that the studies were interpreted post hoc through the framework.
  2. [Abstract and throughout] There are several typographical errors: "collaborativelearning" missing a space in the abstract, "shift in f ocus" with an inserted space, and "instructionaltool" in the introduction. The abstract also uses "APCP framework" without spelling out the acronym there; consider spelling it out on first use.
  3. [Figure 1] Figure 1 is referenced in Section 4 but is not included in the manuscript text. The figure should be provided and should clearly depict the four levels and the relationships among them, ideally including the dimensions along which the levels are said to differ.
  4. [Section 4.1] The phrase "master-servant dynamic" is unnecessarily loaded; "director-executor" or "operator-instrument" would describe the interaction more neutrally and consistently with the paper's conceptual tone.
  5. [Section 5.2] The concept of "Mutual Theory of Mind" is attributed to Frith and Frith (2005), but that source is primarily about human theory of mind. The paper should clarify the sense in which the concept extends to human-AI interaction, or cite a source that explicitly develops this extension.
  6. [Section 4.4] The statement that a Level 4 AI "could pass a domain-specific Turing Test for collaborative competence" is speculative and likely to be read as a stronger claim than intended. Phrasing such as "behaviorally indistinguishable at the task level" would be more precise and less contentious.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: APCP is a conceptual taxonomy, not a fitted derivation; self-citations are illustrative and non-load-bearing.

full rationale

The paper does not present a derivation of results from inputs: it is an explicitly conceptual proposal. The core claim is definitional: "We propose a conceptual framework... that moves beyond the simplistic tool-partner dichotomy to outline four distinct levels of AI agency." The levels are characterized by conceptual criteria (who initiates, degree of autonomy, role in co-construction), and the cited studies are used as illustrations after each level, not as data from which the levels are fitted. No equation, fitted parameter, or statistically forced prediction appears. The self-citations (e.g., the Yan et al. 2025b RCT used to illustrate Level 2) are not load-bearing: they are external empirical studies, falsifiable in their own right, and the framework's structure does not depend on them; the paper even states the framework "is a starting point that invites empirical inquiry and refinement." Similarly, the functional-versus-authentic distinction is introduced as a definitional resolution ("This paper proposes a pragmatic resolution by distinguishing..."), not derived from the framework. The reviewer-level concern that the agency continuum is unidimensional or that levels overlap is a substantive correctness/validation worry, not a circularity that can be exhibited as an equation or a fit. Therefore no circular step is present.

Assumptions & free parameters 1 free parameters · 6 assumptions · 0 invented entities

The paper introduces no fitted numerical parameters beyond the hand-chosen four-level structure. Its axioms are the theoretical and philosophical premises assumed from prior literature, plus the paper-specific assumption that a linear agency continuum is the right organizing frame. No new physical or computational entities are postulated.

free parameters (1)
  • Number of levels in APCP framework = 4
    Author's hand-chosen count; not derived from empirical data or formal theory.
assumptions (6)
  • domain assumption Knowledge is co-constructed through social interaction (Vygotsky; Dillenbourg)
    Adopted from sociocultural learning theory in Section 2 as the foundation for defining collaboration.
  • domain assumption Authentic collaboration requires shared intentionality and theory of mind
    Assumed in Section 5.1, citing Tomasello et al. and Bratman, to argue AI cannot be an authentic collaborator.
  • domain assumption AI systems lack genuine consciousness and semantic understanding
    Assumed in Section 5.1 via Searle's Chinese Room and Dreyfus; this premise drives the authentic/functional distinction.
  • domain assumption Agentic AI is defined by autonomy, proactivity, and goal-directed action
    Definition adopted from literature in Section 3; the framework's need is premised on this being a new category.
  • ad hoc to paper A linear continuum of AI agency is a useful way to organize collaborative learning roles
    The APCP framework's central structural assumption (Section 4); no evidence is given that agency is the primary axis or that levels are exhaustive.
  • ad hoc to paper Functional collaboration can be defined by observable collaborative behaviors and outcomes independent of internal states
    Proposed in Section 5.2 to resolve the authenticity problem; it is a normative definition, not an empirical finding.

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Cite this review

Pith. "Pith review of From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learning." pith.science (2026). https://pith.science/paper/RXZ4GZCK

@misc{pith2026250814825,
  author       = {Pith},
  title        = {Pith review of: From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RXZ4GZCK}},
  note         = {Machine review of arXiv:2508.14825}
}
read the original abstract

The role of Artificial Intelligence (AI) in education is undergoing a rapid transformation, moving beyond its historical function as an instructional tool towards a new potential as an active participant in the learning process. This shift is driven by the emergence of agentic AI, autonomous systems capable of proactive, goal-directed action. However, the field lacks a robust conceptual framework to understand, design, and evaluate this new paradigm of human-AI interaction in learning. This paper addresses this gap by proposing a novel conceptual framework (the APCP framework) that charts the transition from AI as a tool to AI as a collaborative partner. We present a four-level model of escalating AI agency within human-AI collaborative learning: (1) the AI as an Adaptive Instrument, (2) the AI as a Proactive Assistant, (3) the AI as a Co-Learner, and (4) the AI as a Peer Collaborator. Grounded in sociocultural theories of learning and Computer-Supported Collaborative Learning (CSCL), this framework provides a structured vocabulary for analysing the shifting roles and responsibilities between human and AI agents. The paper further engages in a critical discussion of the philosophical underpinnings of collaboration, examining whether an AI, lacking genuine consciousness or shared intentionality, can be considered a true collaborator. We conclude that while AI may not achieve authentic phenomenological partnership, it can be designed as a highly effective functional collaborator. This distinction has significant implications for pedagogy, instructional design, and the future research agenda for AI in education, urging a shift in focus towards creating learning environments that harness the complementary strengths of both human and AI.

Figures

Figures reproduced from arXiv: 2508.14825 by the authors.

Figure 1
Figure 1. The APCP Framework: A Conceptual Framework for AI Agency in Human-AI Collaborative Learning commands. This model aligns with the “user as an operator” or “copilot” metaphor, where the AI is an extension of the user’s will, available for on-demand support (Bradshaw et al., 2013). From a Vygotskian perspective, the AI acts as a powerful mediating artifact, a psychological tool that can augment and reshape cognition bu… view at source ↗

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