Rule-based to Intelligent Chatbots: Understanding Librarian Insights in Library Reference Services

Abstract

Deployment of an artificial intelligence chatbot in libraries has primarily supported reference-oriented and short conversation tasks. The current phase of technological convergence and automation demands a reimagined chatbot capable of sustaining natural, context-aware conversations without human intervention. This study aimed to explore librarians’ insights on readiness, opportunities, and challenges toward implementing artificial intelligence chatbots in reference services. Qualitative research design was employed in this study by using semi-structured interview to gain an in-depth insight from the librarians in the academic libraries. The responses have been analyzed using Nvivo 14 software to organize data into structural findings. The findings showed a favorable indicator of the implementation of artificial intelligence chatbots based on sufficient budget, staff competency, and adequate training. Issues of privacy, relevancy, excessive cost, and human dependency were paramount challenges in the development of artificial intelligence chatbots. Collaborative development between libraries, chatbot service providers, and users will determine the extent of this technology's ability while maintaining the best practice of reference services in the future.

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