A hybrid intent-driven and retrieval-augmented conversational framework for reliable University information access

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dc.contributor.authorNicole Mary Johnson
dc.contributor.authorHumphery Mubiru
dc.contributor.authorEthan Ariko
dc.date.accessioned2026-08-04T08:41:20Z
dc.date.available2026-08-04T08:41:20Z
dc.date.issued2026
dc.date.issued2026-05-28
dc.descriptionUndergraduate
dc.description.abstractStudents at Uganda Christian University frequently struggle to access timely and accurate institutional information because relevant details are scattered across notice boards, social media groups, and outdated web pages. This report presents the design, implementation, and evaluation of a hybrid multi-channel chatbot that merges intent-based routing, keyword retrieval, vector-based semantic search, and large-language-model generation to deliver context-grounded answers drawn from seventeen verified university documents. The system was deployed as both a web application and a WhatsApp integration, offering multilingual support in seven languages through the Sunbird translation service. Functional testing across representative query categories showed that the hybrid retrieval strategy returned relevant context for the majority of test queries, while a confidence-aware fallback mechanism directed users to appropriate university offices when the knowledge base could not provide a reliable answer. The findings indicate that combining complementary retrieval methods with grounded generation is a viable approach for building dependable information assistants in resource-constrained university settings.
dc.description.sponsorshipNone
dc.identifier.urihttps://hdl.handle.net/20.500.12311/3485
dc.language.isoen
dc.publisherUganda Christian University
dc.subjectRetrieval-Augmented Generation (RAG) Hybrid retrieval Intent-driven routing Confidence-gated fallback Grounded large-language-model generation Sentence-transformer embeddings Vector database Conversational AI
dc.subjectUniversity informational chatbot
dc.titleA hybrid intent-driven and retrieval-augmented conversational framework for reliable University information access
dc.typeProject Report

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Bachelor of Science in Computer Science