THE NIGERIAN JOURNAL OF SOCIOLOGY AND ANTHROPOLOGY
ISSN: 0331-4111 e-ISSN: 2736-075X
Volume 24, No. 1, June 2026
Pages 28-49
DOI: 10.36108/NJSA/6202.42.0120
Artificial Intelligence, Academic Workload and Institutional Readiness in African Higher Education: A Rapid Need Assessment
Chinwe Peace Igiri,1,3,7 Judith Ifunanya Ani,2,3,8 Evans Osabuohien,3,5 Ezebunwa E. Nwokocha,4 Gabriel Olukayode Ajayi,1 Juster Gatumi Nyaga,6 Oscar Correia,7,9 and Mebawodu Adebayo Akindele1
1Mountain Top University, Prayer City, Ogun State, Nigeria
2 Walter Sisulu University, South Africa
3DePECOS Institutions and Development Research Centre (DIaDeRC), Ota, Ogun State, Nigeria
4 University of Ibadan, Ibadan, Nigeria
5 Covenant University, Ota, Nigeria
6 Management University of Africa, Kenya
7 Cavendish University Uganda
8 Education and Research for Sustainable Development, Nigeria
9 Cavendish University Zambia
Abstract
This study examined artificial intelligence use, academic workload, and institutional readiness among higher education stakeholders in selected African contexts. It was guided by the rapid emergence of generative artificial intelligence in universities and the need to assess whether institutional policy, training, and governance structures are keeping pace with its adoption. A descriptive rapid needs assessment design was employed. Data were collected using a structured online questionnaire covering respondent characteristics, academic workload, AI use, training needs, preferred AI support functions, ethical concerns, governance priorities, pedagogy, and local relevance. Data were analysed descriptively. Findings indicate that respondents experience substantial workload pressures across research writing, lecture preparation, postgraduate supervision, examination duties, reporting, grading, feedback, and accreditation-related responsibilities. While 80.0% of respondents reported current use of AI tools, fewer than one-quarter (23.3%) had received formal training in AI or digital tools. Nevertheless, nearly all respondents (96.7%) expressed interest in generative AI training. The most preferred AI support functions included research assistance, academic content drafting, grading and assessment support, personalised learning, and curriculum planning. Key concerns identified were academic integrity, data privacy, accuracy, reliability, insufficient training, and potential job displacement. The study concludes that AI adoption in African higher education should be guided by robust institutional policies, structured staff training, human oversight, data protection mechanisms, contextual relevance, and strong ethical safeguards.
Keywords: Academic Workload, African Higher Education, Artificial Intelligence (AI), Institutional Readiness, Responsible AI, Generative AI (Gen AI).