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

2026-04-23

AI Summary

This work proposes a community-centered AI learning framework that challenges the prevailing assumption of generative AI as an unquestioned epistemic authority in education, which often marginalizes learners’ and their communities’ situated ways of knowing. Integrating community-driven learning with constructivist traditions, the framework embeds AI literacy within learners’ lived experiences and local knowledge systems through cognitive fine-tuning, redistribution of epistemic authority, and contextualized judgment. By synthesizing critical AI literacy with collective deliberation mechanisms, it enables learners to collaboratively negotiate the role of AI within their sociocultural, historical, and geographic contexts and to make communal decisions about designing, interrogating, or rejecting AI systems. This approach offers an innovative theoretical pathway toward equitable and contextually responsive AI education.

Technology Category

• Philosophy and Ethics of AI: AI & Epistemology
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Causal Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• epistemic authority
• community-based learning
• AI literacy
• generative AI
• equitable education

Innovation

• community-based AI learning
• epistemic authority
• critical AI literacy
• redistribution of authority
• situated discernment