Community-Based AI Learning: Redistributing Artificial Intelligence's Epistemic Authority in Education

TL;DR

This paper proposes community-based AI learning to shift epistemic authority from AI systems to learners' communities, emphasizing local knowledge, trust calibration, and collective judgment for equitable AI education.

Contribution

It introduces a novel framework grounded in community-driven epistemologies that repositions authority and localizes AI literacy in educational contexts.

Findings

Defines three commitments: epistemic fine tuning, redistribution of authority, situated discernment.

Argues for localizing AI literacy through community engagement and social context.

Highlights the importance of negotiating authority for equitable AI education.

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.