Date
Publisher
arXiv
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.
Study at a glance
- Question: How can AI education ground learning in community knowledge to redistribute epistemic authority away from AI systems?
- Setting and data: Not stated in the paper
- Method: Conceptual analysis (perspective paper)
Why it matters
Generative AI systems risk being positioned as ultimate epistemic authorities in classrooms. However, these systems encode dominant perspectives while discounting learners' community-rooted, lived ways of knowing.
Key findings
- Community-based AI learning reframes AI as one interpretive input rather than an ultimate authority, evaluating AI outputs against situated, lived community knowledge.
- The proposed framework centers on three commitments: epistemic fine-tuning of learner trust, redistribution of epistemic authority, and collective discernment regarding whether to use or reject AI.
- Constructionist artifact creation provides a tangible mechanism for students to negotiate AI authority, making community knowledge the evaluative standard for AI technologies.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Why use AI?
Study design
