EVALUATING THE INTEGRATION OF GENERATIVE AI ON ADMINISTRATIVE WORKFLOW OPTIMISATION IN HIGHER EDUCATION IN NIGERIA

Administration is the quiet machinery that keeps a university running, and in much of Nigerian higher education that machinery still turns by hand. So, when generative artificial intelligence arrived in the mainstream after 2022, it landed on fertile, frustrated ground. Generative AI refers to a class of systems that produce new text, summaries, code and structured documents from natural-language prompts, built on large language models such as those behind ChatGPT, Gemini and Claude (Dwivedi et al., 2023). Unlike earlier automation, which needed rigid rules and clean data, these tools are forgiving; a clerk can draft an admission letter, summarise a policy document, or reconcile a messy spreadsheet simply by describing what they want. That flexibility is precisely why administrative staff, rather than IT departments, have often been the first to pick them up (Chukwuere, 2024; George & Wooden, 2023). This study evaluates how generative artificial intelligence (GenAI) is being integrated into administrative workflows across Nigerian higher education institutions, and whether that integration actually improves efficiency, accuracy and service quality. Drawing on survey responses from 312 administrative staff and academics across twelve federal, state and private universities, alongside eight semi-structured interviews, the research adopts a mixed-methods design anchored in the Technology–Organisation–Environment (TOE) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT). The findings show uneven but growing adoption: GenAI tools are most heavily used for correspondence, admissions processing and student records, and least used for financial and scheduling tasks. Respondents reported statistically significant improvements in turnaround time and perceived service satisfaction after adopting GenAI, though gains in data accuracy were more modest. Unreliable power and internet infrastructure, subscription costs, weak digital skills and the near-total absence of institutional AI policy emerged as the dominant barriers. The paper argues that GenAI in Nigerian higher education administration is currently a bottom-up, improvised phenomenon rather than a governed strategy, and it sets out practical recommendations for policy, capacity-building and infrastructure investment.