ARTIFICIAL INTELLIGENCE ADOPTION AND ADMINISTRATIVE EFFICIENCY: A COMPARATIVE AND DIALECTICAL ANALYSIS OF PUBLIC AND PRIVATE TERTIARY INSTITUTIONS IN RIVERS STATE, NIGERIA

Abstract

The adoption of artificial intelligence in tertiary institutions has generated two opposing but simultaneously true narratives. One narrative presents artificial intelligence as a decisive instrument for reducing administrative bottlenecks, improving record keeping, speeding up decision making and freeing office and information managers from repetitive manual tasks. The other narrative presents artificial intelligence as a source of new administrative burdens, arising from inadequate infrastructure, weak digital literacy, unstable electricity supply, high implementation cost and institutional resistance to change. This paper examines both narratives through a comparative and dialectical analysis of public and private tertiary institutions in Rivers State, Nigeria. Adopting a qualitative, desk based design anchored on the Technology Acceptance Model, the Diffusion of Innovation theory and the Hegelian dialectical method of thesis, antithesis and synthesis, the paper reviews over forty recent local and international empirical and conceptual studies on artificial intelligence adoption and administrative efficiency in higher education. The review shows that public tertiary institutions in Rivers State, being larger, more bureaucratic and more dependent on statutory funding, exhibit slower and more uneven artificial intelligence adoption than private institutions, which tend to be smaller, more centrally managed and more willing to experiment with new administrative technology. At the same time, private institutions face constraints of scale and cost that limit the depth of adoption, while public institutions possess statutory mandates and larger staff strength that could support wider diffusion once infrastructural and policy gaps are closed. The dialectical synthesis proposed in this paper is a hybrid administrative model in which artificial intelligence tools are matched to the resource realities and governance structures of each institutional type rather than adopted uniformly. The paper concludes with policy recommendations for tertiary institution managers, the National Board for Technical Education, the National Universities Commission and government agencies responsible for information and communication technology infrastructure in Rivers State.