Community-Based AI Learning: Redistributing Artificial Intelligence's Epistemic Authority in Education
Santiago Ojeda-Ramirez, Symone Gyles, Kylie Peppler
TLDR
Community-based AI learning redefines AI's role in education, shifting authority from AI to learners' lived and community epistemologies for equitable learning.
Key contributions
- Proposes "community-based AI learning" to ground AI authority in local epistemologies.
- Articulates three commitments: epistemic fine tuning, authority redistribution, and situated discernment.
- Localizes critical AI literacy by calibrating trust and foregrounding community knowledge.
- Supports collective judgment on when to design with, interrogate, or reject AI systems.
Why it matters
This paper is crucial as it challenges AI's perceived authority in education. It offers a vital framework for creating more equitable and contextually relevant AI learning experiences, empowering communities to align AI education with local values and knowledge.
Original Abstract
As generative AI systems increasingly mediate learning, they are often treated as authoritative sources of knowledge. This perspective paper introduces community-based AI learning as a framework that repositions authority, grounding AI engagement in learners' lived and community-based epistemologies. Drawing from community-driven learning and constructionist traditions, we articulate three commitments: epistemic fine tuning, redistribution of authority, and situated discernment. Together, these processes localize critical AI literacy by calibrating trust, foregrounding community knowledge, and supporting collective judgment about when to design with, interrogate, or reject AI. We argue that equitable AI education requires negotiating authority through place, history, and social context.
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