Proceedings

Brazilian Indigenous Languages Revitalization at Scale: Reducing Cost and Development Time for Low-Resource Language Courses

Gustavo Polleti, Fabricio Gerardi, Carolina Aragon, Fernando Bororo, Mariel Kujiboekureu, Wayali Salomã, Fabio Cozman

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. AI and Social Good. Pages 7408-7417.

https://doi.org/10.24963/ijcai.2026/824

Keywords

  • Natural Language Processing: Natural Language Processing
  • Humans and AI: Humans and AI
  • Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications
  • AI Ethics, Trust, Fairness: AI Ethics, Trust, Fairness

Abstract

Brazil is home to about 180 Indigenous languages, which span a wide range of sociolinguistic circumstances, from critically endangered to relatively stable. Across this continuum, Indigenous communities often lack pedagogical resources and are underserved by existing language-learning technologies, which are typically designed for high-resource languages and assume solid connectivity and large datasets. This research project proposes AI-assisted tools and workflows for the collection and annotation of textual and speech data that substantially reduce the time and cost required to produce engaging language-learning game apps. Our goal is to implement a language-learning game app that Indigenous students can use to practice their reading, writing and speaking skills at home. We propose novel speech processing models for low-resource Indigenous languages and offlline support in low-connectivity environments. Our project adopts a co-creation model that actively foster collaboration between Indigenous educators, linguists, and youth, is adapted to the their context, and complies with ethical guidelines. We outline an implementation plan with Bororo and Enawene Nawe communities to test our methods and, potentially, produce an AI-driven platform for Indigenous language education that is applicable across diverse sociolinguistic contexts in Brazil and beyond.

Gustavo Polleti, Fabricio Gerardi, Carolina Aragon, Fernando Bororo, Mariel Kujiboekureu, Wayali Salomã, and Fabio Cozman. 2026. Brazilian Indigenous Languages Revitalization at Scale: Reducing Cost and Development Time for Low-Resource Language Courses. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 7408–7417. https://doi.org/10.24963/ijcai.2026/824
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