Generative AI Adoption in Nigerian Higher Education

Abstract

This study examined the adoption of generative AI in Nigerian higher education through a mixed methods approach, integrating quantitative and qualitative techniques to comprehensively assess the benefits, challenges, and opportunities. The Federal University of Technology served as the sampling site. A stratified random sampling strategy was employed to select approximately 312 survey respondents, ensuring diverse representation across gender, academic rank, and discipline. Additionally, purposive sampling was used to select 20 experienced generative AI users and policymakers for in-depth interviews. The collected data were analyzed using descriptive statistics, correlation, and regression analysis. Findings revealed that 98.7% of respondents are willing to use generative AI if provided with adequate training. Current usage rates stand at 51.9% for teaching and 41.6% for research, while utilization for mentoring and administrative tasks remains low. Variations in familiarity with generative AI tools influenced usage patterns, underscoring the need for targeted training programs. Key benefits of adopting generative AI include enhanced research support and creative assistance. However, challenges such as ethical concerns (plagiarism and academic integrity), skill gaps, and limited access to resources were identified. Despite these obstacles, the potential of generative AI to enhance productivity in higher education—particularly in the creation of teaching and research materials—remains substantial. The study recommends that Nigerian higher education institutions implement proactive policies, including staff training, infrastructure development, the establishment of ethical guidelines, and integration of generative AI into curricula to effectively harness its transformative potential.

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