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.
Why it matters
Framing generative AI as an impartial educational tool overlooks how it reassigns authority from teachers to students and machines. Furthermore, current frameworks fail to address disparities in power distribution, data ownership, and accountability in AI-mediated learning.
Key findings
- In classrooms, generative AI shifts authority over knowledge from teachers and students to machines, altering who creates and validates evidence of learning.
- Existing educational technology theories fail to address ongoing disparities in power distribution, data ownership, and accountability during AI-mediated interactions.
- Shared agency between humans and AI involves relational, regulatory, and teaching processes that require human accountability for knowledge claims.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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