Librarians’ Self-Regulated Learning and Use Of Artificial Intelligence Tools for Service Delivery in Federal Universities in Southwestern Nigeria

Abstract

This study investigated librarians’ self-regulated learning (SRL) and the use of artificial intelligence (AI) tools for service delivery in federal university libraries in Southwestern Nigeria. A descriptive survey research design of the correlational type was employed. The population consisted of professional librarians across the six federal universities located in Lagos, Ogun, Oyo, Osun, Ondo, and Ekiti States, with a total sample of 111 librarians selected through census approach. Data were collected using a validated structured questionnaire titled Self-Regulated Learning and Use of Artificial Intelligence Tools for Library Service Delivery Questionnaire (SRL-AI-LSDQ). Descriptive statistics such as frequency counts, means and standard deviations, as well as inferential statistics including Pearson Product Moment Correlation Coefficient (PPMCC) were used for analysis. Findings of the study revealed a high level of awareness of AI tools (x̄ = 3.12) and strong acceptance (x̄ = 3.26), though actual application remained moderate (x̄ = 2.58). The most frequently applied tools were AI-powered search assistants (x̄ = 3.05) and chatbots (x̄ = 2.95), while tools like robotic process automation (x̄ = 2.09) and natural language processing (x̄ = 2.45) had low usage. Librarians demonstrated moderate engagement in self-regulated learning (x̄ = 2.74), particularly in goal-setting and awareness dimensions. A significant positive correlation (r = 0.910, p < 0.05) was found between self-regulated learning and AI tool usage, indicating that librarians with stronger SRL capacities were more likely to apply AI tools effectively. The study concluded that fostering SRL is essential to improving AI adoption and enhancing service delivery. It recommended strategic training and capacity-building initiatives to strengthen SRL practices and technological integration in academic libraries.