Background of the study
AI‑powered chatbots provide real‑time assistance to library patrons, handling queries ranging from catalog searches to service hours and event information (Khan, 2023). Equipped with natural language understanding and integration with library databases, chatbots can operate 24/7, improving accessibility and reducing staff workload (Johnson, 2024). Studies demonstrate that library chatbots can resolve up to 70% of routine inquiries without human intervention, enhancing user satisfaction (Nguyen, 2025). At Usmanu Danfodiyo University Library, a multilingual AI chatbot supporting English and Hausa was deployed to cater to diverse user groups, yet its effectiveness in accurately understanding regional dialects and integrating with the library’s knowledge base has not been evaluated. Critical factors include training data representativeness, dialog flow design, and fallback mechanisms for complex queries (Eze, 2025). This investigation analyzes chatbot logs, user feedback, and comparative performance against human reference services to assess AI chatbot effectiveness in meeting patron needs within a Sokoto State academic library setting (Okoro, 2024).
Statement of the problem
Despite chatbot availability, many patrons revert to email or in‑person inquiries, suggesting shortcomings in chatbot accuracy, language support, or user awareness. No formal study has identified the root causes or improvement strategies.
Objectives of the study
To evaluate chatbot accuracy and comprehension of library queries in English and Hausa.
To measure user satisfaction and adoption rates.
To identify enhancements for dialog design and knowledge base integration.
Research questions
What percentage of user queries are successfully resolved by the AI chatbot?
How do users rate chatbot helpfulness and language support?
What system improvements are needed to increase chatbot effectiveness?
Significance of the study
The study will guide library IT and service teams in refining chatbot training, expanding language capabilities, and promoting AI tools, thereby enhancing user engagement and operational efficiency.
Scope and limitations of the study
This investigation focuses on the AI chatbot at Usmanu Danfodiyo University Library. It excludes other virtual reference channels and external chat services.
Definitions of terms
Dialog flow: The structure and sequence of interactions between a chatbot and user.
Fallback mechanism: A chatbot feature that routes unresolved queries to human staff.
Multilingual support: Capability of AI systems to process and respond in multiple languages.
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