Community-Based AI Learning: Redistributing Artificial Intelligence's Epistemic Authority in Education
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913058241118208 |
|---|---|
| author | Ojeda-Ramirez, Santiago Gyles, Symone Peppler, Kylie |
| author_facet | Ojeda-Ramirez, Santiago Gyles, Symone Peppler, Kylie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21986 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Community-Based AI Learning: Redistributing Artificial Intelligence's Epistemic Authority in Education Ojeda-Ramirez, Santiago Gyles, Symone Peppler, Kylie Human-Computer Interaction 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. |
| title | Community-Based AI Learning: Redistributing Artificial Intelligence's Epistemic Authority in Education |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.21986 |