Volume 4,Issue 3
Research on the Construction of Artificial Intelligence-Driven Smart Information Service Models in University Libraries
In the context of the rapid development of Artificial Intelligence (AI) technology, university libraries are facing key opportunities and challenges in their transformation from traditional services to smart services. This paper delves into the construction of AI-driven smart information service models for university libraries. By analyzing the current development status and existing problems of smart services in university libraries, and integrating the application scenarios and advantages of AI technology, it proposes a multi-dimensional smart information service model framework covering resource integration, user service, and management operations. The implementation pathways and guarantee mechanisms are systematically elaborated. The research aims to provide theoretical references and practical guidance for university libraries to enhance service efficiency, meet the diverse knowledge needs of faculty and students, and promote the construction of smart libraries.
[1] Zhang HW, Wang YX, 2023, Research on the Construction of Intelligent Knowledge Service System for University Libraries Based on Knowledge Graph. Journal of Library Science, (15): 2-11, 22.
[2] Zhao Y, Wu D, 2022, Library Space Service Innovation in the Era of Digital Intelligence: From Intelligent Sensing to Scenario Construction. Journal of Library Science in China, 48(6): 49-68.
[3] Shen S, Liu J, Chen D, 2023, Research on Role Reshaping and Capacity Development of University Library Librarians in the Artificial Intelligence Environment. Journal of Academic Libraries, 41(2): 82-88.
[4] Liu K, Jia YN, 2024, Interaction Design and Effect Evaluation of Library Intelligent Consulting Services from the Perspective of Human-Machine Collaboration. Information Studies: Theory & Application, 47(2): 127-135.
[5] Hao YL, 2024, Research on the Construction of Digital Publishing Ecosystem. Hebei Science and Technology Library Garden, 37(6): 3-9.
[6] Elfwing S, Uchibe E, Doya K, 2017, Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning. Neural Networks, 107: 3-11.