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Reclaiming Epistemic Agency: A Critical Framework for Human-Generative AI Co-Agency in Education

Synthesis: Poudyal (2026) argues that generative AI in education does more than add a new tool: it reassigns epistemological authority from teachers to students to machines, redistributing Agency across humans and non-human systems in a process the paper terms co-agency. After evaluating Distributed Agency, Self-Determination Theory, Society 5.0, and integration frameworks such as TPACK and SAMR — none of which address equitable power, data ownership, or accountability — the paper introduces the Ecological Co-Agency Framework, defining agency through relational, regulatory, and pedagogical processes bounded by a non-negotiable condition of human epistemic accountability. The framework offers educators and policymakers a more precise vocabulary than "balance" for deciding how tasks, evaluation, and responsibility are distributed between learners and AI in higher education.

From tool to co-agent: the epistemic shift

Generative AI has largely been framed as an impartial educational tool, and most empirical research has examined its effects on student interest, teacher workload, and assessment validity. Poudyal argues this framing overlooks a deeper shift: the reassignment of who produces knowledge, validates claims, and creates evidence of learning. Because GenAI can generate content, evaluate its own output, and adapt without direct human involvement, it participates in the evaluation process and thereby acts within the context of the Agency it represents. This redistribution of agency from human to non-human entities is what the paper calls co-agency (drawing on Godwin-Jones, 2024). The epistemic concern is not that GenAI may produce unreliable content, but that its ease of use may obscure whose judgment — human or machine — ultimately underlies an assertion, determination, or product.

Why existing frameworks fall short

The literature review evaluates four theoretical traditions that have been applied to AI-mediated agency:

  • Distributed Agency and social-material views treat competency, Creativity, and judgement as products of relationships among student, tool, and context — yet do not address who owns the interaction data or who is accountable.
  • Self-Determination Theory (Ryan & Deci, 2000) identifies autonomy, competence, and relatedness as drivers of Motivation; GenAI changes the circumstances under which these occur but does not resolve equity of power.
  • Society 5.0 offers a "human-centered" model of digital–physical symbiosis, but Harari's caution that GenAI autonomously creates and assesses knowledge claims warns against assuming its equilibrium will be automatic.
  • TPACK and SAMR integration frameworks (including contextual-knowledge extensions and substitution-level adoption critiques) focus on teacher knowledge and adoption levels rather than on power, data management, and ethical responsibility.

None of these treats GenAI integration as a primary issue of Agency or articulates the ethically justifiable conditions under which the dimensions can be combined — the gap the framework fills.

The Ecological Co-Agency Framework

The framework treats co-agency as the relationship between three interdependent, co-constituted dimensions, all bounded by a non-negotiable condition of human epistemic accountability:

  • Relational co-agency defines agency as resulting from interaction rather than residing in person or machine. It requires (1) a transparent description of which tasks are assigned to AI and which to students, and (2) ethical co-agency — a joint responsibility for an educational outcome in which humans retain primary accountability and act in a monitoring capacity.
  • Regulatory co-agency maps Zimmerman's three phases of self-regulated learning (forethought, performance, reflection) onto AI-mediated environments. GenAI can assist with objectives and sources in forethought, offer feedback and prompts during performance (what Dede terms "augmented intelligence" or Scaffolding), and produce progress summaries in reflection — but the point in the SRL cycle at which GenAI enters by default determines whether it amplifies or erodes the learner's sense of control. The paper notes that strategic Cognitive Offloading can support transformative learning when the offloading decision is intentional rather than routine.
  • Pedagogical co-agency places the teacher at the center of integration, positioned along a continuum from observer to adopter to collaborator to innovator (Zhai, 2025) and shaped by institutional support in time, training, and technology. Without structural support, the continuum becomes less a developmental tool and more a means of identifying teachers who already possess resources to experiment. Teachers report being assigned substantial invisible labor reviewing, editing, and rewriting AI-generated materials — an example of professional judgment exercised precisely where AI cannot.

The ethical boundary condition constrains all three dimensions through three requirements: (1) contestability — learners and teachers must be able to question and cross-check AI output, requiring some degree of explainability; (2) provenance — institutions should describe the sources of training data and how they introduce bias; and (3) non-delegation of moral and intellectual credit — decisions about student welfare, academic standing, and high-stakes judgments must not be determined solely by GenAI.

Implications for assessment and policy

The framework's concrete implications reshape both practice and governance. In Assessment, instructors move from evaluating a completed essay as a product toward evaluating the "decision trail" visible throughout the writing process — which tasks were delegated, how suggestions were accepted, modified, or rejected. In institutional procurement, transparency requirements turn purchasing into an exercise of epistemological governance, a demand that is particularly difficult for low-income districts already facing a digital divide and uneven AI literacy, so GenAI may widen the gaps it intends to close. For policymakers, the framework translates vague principles of transparency, fairness, and oversight into specific questions about division of labor, placement of GenAI in the learning cycle, institutional investment in teachers, and whether learners are permitted to challenge AI outputs — connecting to Equity In AI Education, Governance, and the philosophy of ethical AI design.

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Citation

Poudyal, B. (2026). Reclaiming epistemic agency: A critical framework for human-generative AI co-agency in education. arXiv:2608.26937.

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