A personalização do serviço de referência em bibliotecas universitárias com o uso da inteligência artificial generativa

Abstract

Objetivo: Verify through the literature how university libraries are using Generative Artificial Intelligence to enhance user service by making it personalized. Methods: This is a bibliographic, exploratory-descriptive research, with a qualitative-quantitative approach, the corpus of the research was taken from the Web of Science and Scopus, through a narrative literature review. Data was collected between 2019 and 2024. Bardin's (2011) categorical content analysis was carried out. VOSviewer software was also used to analyze the frequency of keywords in the papers. Results: It was observed that the use of generative artificial intelligence has the potential to translate the user's preferred languages, provide cohesive and authentic responses, recommend relevant content to users based on their profile, and can speed up interactions between services and people. Generative AI is thus emerging as a potential tool for offering more personalized services. However, there are challenges, such as the production of incorrect answers, the inability to understand human emotions/expressions, privacy and the quality of sources. Conclusions: Generative technologies have enormous potential to enhance the quality of reference responses, meet the needs of virtual reference services, and personalize content for users.