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

This paper describes Kwame for Science, an AI teaching assistant deployed in West Africa that answers students' science questions by retrieving relevant passages from curated knowledge sources and past national exam questions, achieving 87.2% top-3 accuracy over 8 months with 750 users across 32 countries. The system uses SBERT for semantic similarity matching and includes automatic topic categorization of exam questions aligned to the West African Senior Secondary Certificate Examination syllabus.

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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