READINESS AND COMPETENCE OF ADOPTING ARTIFICIAL INTELLIGENCE (AI) FOR SERVICE DELIVERY IN STATE UNIVERSITY LIBRARIES IN NORTH EAST, NIGERIA

This study examined the readiness and competence of staff in adopting Artificial Intelligence (AI) for service delivery in state university libraries in North-East Nigeria. Specifically, the study sought to assess the level of institutional readiness for AI adoption, evaluate the competence of library staff in AI-based service delivery, and identify challenges hindering AI integration. A descriptive survey design was adopted, and the entire population of 115 librarians from six state university libraries was targeted, with 103 valid responses obtained. Data were collected using a validated questionnaire and analyzed using descriptive statistics (means and standard deviations) and Pearson correlation to test the stated hypothesis at a 0.05 significance level. The findings reveal that the level of readiness for AI adoption in the libraries is generally low (Cluster Mean = 1.87, SD = 0.58), with deficiencies in technological infrastructure, budget allocation, institutional support, and collaboration with AI technology providers, despite a generally positive attitude toward innovation and AI’s future relevance. Library staff demonstrated moderate conceptual understanding of AI applications (Cluster Mean = 2.41, SD = 0.97) but low practical competence and technical proficiency in operating AI tools, including data analytics, machine learning interfaces, and AI-driven search platforms. Challenges hindering AI adoption were identified as inadequate funding, poor internet connectivity, lack of technical skills and training, resistance to change, weak management support, and insufficient collaboration with technology experts (Cluster Mean = 3.32, SD = 0.86). Pearson correlation analysis revealed a weak but statistically significant positive relationship between library readiness and staff competence (r = 0.18, p = 0.04), suggesting that institutional preparedness slightly influences staff competence, though other factors such as training and infrastructure remain critical. The study concludes that successful AI adoption in state university libraries requires simultaneous improvements in institutional readiness and staff competence. Strategic interventions including targeted training, infrastructure enhancement, policy development, and resource allocation are recommended to overcome barriers and facilitate effective AI-driven library service delivery.