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

Santiago Ojeda-Ramirez, Symone Gyles & Kylie Peppler (2026)

Synthesis: A perspective paper introducing community-based AI learning as a framework that repositions epistemic authority, grounding AI engagement in learners' lived and community-based epistemologies rather than treating AI systems as authoritative knowledge sources. Drawing on community-driven learning and constructionist traditions, the framework rests on three commitments—epistemic fine-tuning, redistribution of authority, and situated discernment—that together localize critical AI literacy by calibrating trust, foregrounding community knowledge, and supporting collective judgment about when to design with, interrogate, or reject AI. The authors argue that equitable AI education requires negotiating authority through place, history, and social context.

Key Findings

• Generative AI systems are frequently treated as authoritative knowers in classrooms: their confident, fluent outputs and widespread institutional adoption grant them "epistemic authority," yet this authority is not neutral but historically structured through power relations that marginalize community-based, Indigenous, and Global South epistemologies in favor of dominant Eurocentric formations.
• Dominant AI-education paradigms design AI as instructional agents (tutors, co-learners), implicitly positioning AI systems as default reference points for what counts as correct, relevant, or complete knowledge—particularly where learners lack prior expertise.
• Existing equity-oriented work (computational empowerment, critical AI literacy, youth AI auditing and design) redistributes technical agency but often stops short of centering learners' communities as primary epistemic resources for interpreting AI systems.
• Community-based AI learning is defined as an approach to AI education in which learners engage with AI through issues, practices, and forms of knowledge grounded in their communities, treating local experience and lived realities as central resources for learning with and about AI.
• The framework rests on three commitments: epistemic fine-tuning (learner-centered calibration of how AI outputs are interpreted and trusted, comparing them against lived experience, local histories, and community expertise); redistribution of authority (refusing AI as ultimate authority, positioning learners and communities as legitimate knowers, and cultivating technoskepticism); and situated discernment (collective examination of how AI infrastructures intersect with social worlds, with criticality shaped by place, geography, and local histories).
• In practice, learners design something real for their community using AI as a design resource while community knowledge and community practitioners serve as the evaluative standard; the approach can function modularly alongside technical instruction.

Implications for AI in Education

Community-based AI learning reframes the goal of AI education away from treating AI systems as credible, authoritative sources of explanation and toward what the authors call epistemic fine-tuning: a learner-centered recalibration of how AI outputs are interpreted, trusted, and used. Rather than assuming AI is a resource to be consulted, calibrated, or corrected, the framework positions learners and their communities as legitimate knowers whose situated local knowledge holds interpretive authority over AI outputs.

This has concrete consequences for curriculum and pedagogy. For educators, the framework calls for structured opportunities for students to evaluate AI outputs against locally grounded criteria of relevance, harm, and usefulness—positioning AI as one interpretive resource within collective sense-making rather than abandoning technical rigor. For curriculum designers and policymakers, it shifts critical AI literacy from generalized awareness of sociopolitical issues toward situated engagement with AI's consequences for a specific community, including contexts where critical engagement may require limitation, refusal, or strategic non-use rather than building with AI.

The framework's three commitments—epistemic fine-tuning, redistribution of authority, and situated discernment—connect AI learning to constructionist traditions, in which learners externalize their understanding through the creation of shareable artifacts, and to community-driven learning that treats community knowledge as the evaluative standard. It grounds learner agency in communities' collective judgment about the relationships they want to have with technology, rather than in individual mastery alone.

Connected Concepts

• AI Literacy
• Constructivist
• Agency
• Human AI Collaboration
• AI Education
• Culturally Relevant Pedagogy

Connected Articles

• Reducing AI Misuse
• Equity In AI Education

Citation

Ojeda-Ramirez, S., Gyles, S., & Peppler, K. (2026). Community-Based AI Learning: Redistributing Artificial Intelligence's Epistemic Authority in Education. Proceedings of the 2026 Conference for Research on Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT 2026). ACM.