Navigating the Challenges of Artificial Intelligence Adoption in Nigerian University Management - Iyanuoluwa Samuel Adelakun; Mayokun Adeyoola Adelakun; Sariat Iyabo Salaam; Stephen Olanrewaju Oni; Bukola Roseline Aruleba (Universitas Islam Bunga Bangsa Cirebon, 2026)
Artificial Intelligence (AI) is transforming higher education globally by improving administrative efficiency, enhancing teaching and learning, and supporting data-driven decision-making. In Nigeria, however, AI adoption within university management remains limited due to infrastructural deficits, insufficient technical expertise, resistance among academic staff, fragmented policies, and ethical concerns. This study examines the challenges to AI integration in Nigerian universities and proposes strategies for sustainable adoption. It adopts a Structured Systematic Literature Review (SSLR) with conceptual synthesis. A total of 60 peer-reviewed studies and policy-related publications published between 2020 and 2026 were identified, screened, and synthesised to examine evidence on AI adoption in higher education management. Thematic analysis was employed to identify recurring patterns relating to technological readiness, human capacity, institutional culture, governance, and regulatory frameworks. The findings indicate that although awareness of AI's potential is increasing, implementation remains constrained by systemic deficiencies in infrastructure, digital competencies, institutional support mechanisms, policy coherence, and ethical oversight. The study further reveals that successful AI adoption requires alignment among technological capabilities, organisational processes, human competencies, and responsible governance structures. It concludes that integrating AI into Nigerian university management is not merely a technological initiative but a broader socio-institutional transformation. By synthesising theoretical and empirical insights, the study contributes to the growing discourse on digital transformation in higher education and offers context-specific recommendations for policymakers, university administrators, and educators seeking effective and sustainable AI implementation strategies.