Date
Publisher
arXiv
Generative artificial intelligence (GenAI) has been primarily framed as an impartial educational tool. However, this framing overlooks an even larger shift: the reassignment of epistemological authority from teachers to students to machines. This paper presents a conceptual evaluation of the extent to which GenAI redistributes students' and teachers' ability to act in classrooms to produce knowledge, validate each other's claims, and create evidence of student learning while collaborating with and competing against humans. This evaluation draws on various theoretical paradigms, including Distributed Agency, Self-Determination Theory, Society 5.0, and Technology Integration Paradigms, including TPACK and SAMR. While all of the theoretical paradigms evaluated are relevant to the role of agency within education mediated by AI, none of them address the ongoing disparity regarding equitable distribution of power, ownership of the data used to mediate interaction, and accountability in relation to human-mediated interactions. As such, this paper introduces the Ecological Co-Agency Framework, which defines agency in terms of relational, regulatory, and pedagogical processes, conditioned by a defined commitment to human accountability for epistemological claims.
Study at a glance
- Question: How does generative AI affect student and teacher agency, and what mechanisms can enable its equitable, responsible incorporation?
- Setting and data: Literature from Scopus, Web of Science, ERIC, and Google Scholar, primarily published post-2018.
- Method: Conceptual synthesis and model development using purposeful, iterative searches and citation chaining.
Why it matters
Generative AI reassigns epistemic authority from teachers to students to machines. Existing integration models overlook power disparities, data ownership, and human accountability when AI systems produce persuasive educational content.
Key findings
- The Ecological Co-Agency Framework defines human-AI co-agency across three interdependent dimensions: relational, regulatory, and pedagogical.
- Human epistemic accountability serves as an essential boundary condition requiring contestability, data provenance, and the non-delegation of moral and intellectual responsibility.
- A student's regulatory agency depends heavily on whether generative AI is introduced before an attempt or during subsequent reflection phases.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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