Using Agentic AI to Enhance the Quality of Academic Libraries’ Responses for Student Queries

Abstract

This study examines the application of agentic AI to evaluate the effectiveness, accuracy, and efficiency of academic libraries’ responses to student queries. Data were collected by analyzing frequently asked questions (FAQs) from the University of Wisconsin-Milwaukee library website using OpenAI's large language models (LLMs), o4-mini. The analysis focuses on identifying the types of questions that are most appropriate for AI-assisted responses and how agentic AI can support librarians in delivering targeted and timely assistance. The findings reveal that agentic AI can significantly improve the quality of library services by providing more timely and targeted student support. This research aims to enhance the effectiveness of librarian responses, strengthen librarian-student interactions, and support the meaningful integration of agentic AI into academic libraries.