@inproceedings{tapo-etal-2025-gaife,
    title = "{GAI}f{E}: Using {G}en{AI} to Improve Literacy in Low-resourced Settings",
    author = "Tapo, Allahsera Auguste  and
      Coulibaly, Nouhoum  and
      Diallo, Seydou  and
      Diarra, Sebastien  and
      Homan, Christopher M  and
      Keita, Mamadou K.  and
      Leventhal, Michael",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.442/",
    pages = "7914--7929",
    ISBN = "979-8-89176-195-7",
    abstract = "Illiteracy is a predictor of many negative social and personal outcomes. Illiteracy rates are particularly high in countries with underresourced languages, where few books exist that are suitable for children to learn to read from. We present GAIfE (Generative AI for Education), a toolchain and workflow developed through empirical methods, that demonstrates how existing tools can be adapted to address low literacy for an underresourced language. We used GAIfE (a play on the Bambara word for ``book'') to construct materials for developing children{'}s reading competence in Bambara, the vehicular language of Mali. Our approach to the generation and post-generation editing of content skewed by the Global-North-centric bias of available LLMs, enabled us to rapidly multiply the content in Bambara available online by 10 times while maintaining high standards of attractiveness of the material to maintain high engagement, accurate representation of the Malian culture and physical and social environment and language quality. Using our materials, pilot reading programs achieved a 67{\%} reduction in the number of children unable to read Bambara. Our approach demonstrated the power of bias-aware application of generative AI to the problem domain as well as the potential impact the application of this technology could have on reducing illiteracy and improving learning outcomes through native language education."
}