Authors - Simon Kloker, Alex Cedric Luyima, Matthew Bazanya Abstract - This paper presents WASHtsApp, a WhatsApp-based mHealth chatbot that supports clean water, sanitation, and hygiene (WASH) education in rural African settings. The chatbot uses Retrieval-Augmented Generation (RAG) to reduce out-of-context responses and improve answer relevance. Following a Design Science Research approach, we evaluated the artifact in two steps: expert content validation (four WASH experts) and community acceptance validation (n = 71). Expert ratings classified 86% of responses as perfect or sufficient, while community results showed high perceived usefulness, ease of use, and intention to use. The findings indicate that WhatsApp is a viable delivery channel for WASH education and that a constrained RAG setup can provide useful localized guidance. We also discuss privacy, safety, and future improvements, including local-language support.
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