Real-World Deployment and Evaluation of Kwame for Science, An AI Teaching Assistant for Science Education in West Africa

This paper describes the development, real-world deployment, and evaluation of Kwame for Science, an AI teaching assistant that answers science questions for West African secondary school students preparing for the WASSCE exam, using SBERT-based retrieval to provide relevant passages and past exam questions. The system was deployed over 8 months with 750 users across 32 countries and achieved 87.2% top-3 accuracy on a test set of 109 questions.

Africa has a high student-to-teacher ratio which limits students' access to teachers for learning support such as educational question answering. In this work, we extended Kwame, a bilingual AI teaching assistant for coding education, adapted it for science education, and deployed it as a web app. Kwame for Science provides passages from well-curated knowledge sources and related past national exam questions as answers to questions from students based on the Integrated Science subject of the Wes

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