ADOPTION OF AI-ENABLED PREDICTIVE ANALYTICS FOR LIBRARY RESOURCE MANAGEMENT BY ACADEMIC LIBRARIANS AT KASHIM IBRAHIM LIBRARY, AHMADU BELLO UNIVERSITY, ZARIA

Abstract

This study examines the adoption of AI-enabled predictive analytics for library resource

management by academic librarians at Kashim Ibrahim Library (KIL), Ahmadu Bello

University (ABU), Zaria. The research adopted a survey design to collect quantitative data

from the entire population of 203 academic librarians through a self-structured questionnaire.

The instrument was designed to assess the extent of AI adoption, perceived benefits, and

challenges faced by librarians in integrating AI-powered tools into library resource

management. Data were collected via an online survey platform. The findings reveal that while

there is significant potential for AI-enabled predictive analytics to enhance resource allocation

and decision-making, its adoption at KIL remains limited. Key benefits include improved

resource planning, but challenges such as lack of technical expertise, funding constraints, and

inadequate infrastructure hinder full implementation. The study concludes by recommending

that KIL invest in technological infrastructure, develop comprehensive AI training programs

for staff, and establish an institutional policy on AI adoption to facilitate the integration of

predictive analytics in resource management.