GAIfE: Using GenAI to Improve Literacy in Low-resourced Settings
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 utilized 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.
Despite the Global-North-centric bias of available LLMs, GAIfE 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 applying 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.
Paper Type: Long
Research Area: Multilingualism and Cross-Lingual NLP
Research Area Keywords: Multilinguality and Language Diversity, Resources and Evaluation
Contribution Types: Approaches to low-resource settings, Data resources
Languages Studied: Bambara
Submission Number: 3891
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