INTEGRATION OF ARTIFICIAL INTELLIGENCE INTO RECORDS MANAGEMENT IN TERTIARY INSTITUTIONS IN ZAMFARA STATE, NIGERIA EMPIRICAL EVIDENCE FROM LISTED MANUFACTURING FIRMS IN NIGERIA Section Articles

Abstract

This study examined the integration of Artificial Intelligence (AI) into records management in
tertiary institutions in Zamfara State, Nigeria. Specifically, the study evaluated the existing
records management systems, assessed infrastructural and human capacity readiness for AI
adoption, identified the structural, technical, and organizational barriers to AI integration,
and determined the perceived impact of AI on the efficiency, security, and accessibility of
records management. A descriptive survey research design was adopted. The study population
comprised administrators, registry/records officers, ICT personnel, and librarians directly
involved in records management activities across six selected tertiary institutions in Zamfara
State: Federal University Gusau; Zamfara State University, Talata Mafara; Federal College
of Education (Technical), Gusau; Federal Polytechnic, Kaura Namoda; Abdu Gusau
Polytechnic; and College of Education, Maru. A sample of 398 respondents was selected using
the Krejcie and Morgan (1970) sampling table with proportionate allocation across the six
institutions. Data were collected using a structured questionnaire validated by experts, while
the reliability of the instrument was established using Cronbach's Alpha. Descriptive statistics
comprising frequency, percentage, mean, and standard deviation were used to answer the
research questions, while simple linear regression was employed to test the hypothesis at the
0.05 level of significance. The findings revealed that the selected institutions have functional
records management systems (Grand Mean = 3.74), and respondents indicated a moderate
level of infrastructural and human capacity readiness for AI adoption (Grand Mean = 3.55).
The study further identified inadequate ICT infrastructure, insufficient funding, lack of
technical expertise, resistance to organizational change, data security concerns, and
inadequate institutional policies as major barriers to AI integration (Grand Mean = 3.91).
Respondents also agreed that AI integration would significantly improve the efficiency,
security, accessibility, and overall effectiveness of records management (Grand Mean = 3.98).
Regression analysis showed that AI integration has a significant positive influence on records
management (R² = 0.611, β = 0.782, p < 0.05). The study concluded that effective AI
integration requires adequate infrastructure, institutional support, and staff capacity
development. It recommended increased investment in ICT infrastructure, staff training, policy
development, and phased implementation of AI technologies to enhance records management
in tertiary institutions.