Patterns and Frequency of Artificial Intelligence Utilization for Academic Purposes among Undergraduate Students of Lagos State University

Amos Ayomide Olomu¹, Olasupo O. Ariyibi²

¹ Researcher, Science and Technology Education, Lagos State University, Lagos, Nigeria.
² Lecturer, Science and Technology Education, Lagos State University, Lagos, Nigeria

Abstract

The integration of Artificial Intelligence (AI) into higher education has fundamentally transformed how students’ access, process, and produce academic knowledge. This study investigates the frequency, patterns, and determinants of AI tool utilization among undergraduate students at Lagos State University (LASU), Nigeria. Employing a descriptive survey design, data were collected from 100 students across all academic levels (100–400) and diverse faculties via a structured Google Form questionnaire. Findings reveal that 87% of respondents use AI tools weekly or daily for academic tasks such as essay writing, grammar correction, paraphrasing, research support, and exam preparation. The most frequently used tools include ChatGPT (79%), Grammarly (68%), QuillBot (54%), and Turnitin (42%). A significant difference in usage frequency was observed across academic levels (χ² = 9.31, p = 0.023), with 300- and 400-level students reporting more strategic and consistent engagement due to increased research demands. Despite high adoption rates, challenges such as unstable internet connectivity (61%), limited access to digital devices (47%), ethical concerns about plagiarism (38%), and inadequate digital literacy (33%) persist. The study concludes that AI has become an indispensable component of smart study culture at LASU. It recommends that the university develop institutional AI policies, integrate AI literacy into curricula, expand digital infrastructure, and provide training for both students and lecturers to ensure equitable, ethical, and effective integration of AI in academic practice.    

Keywords: Artificial Intelligence, AI in education, Smart study, Academic performance, Digital literacy, Frequency of use, Lagos State University.

References

1. Adebayo, T. and Ogunleye, R. (2023) ‘Digital confidence and academic success: Exploring the impact of AI tools on student self-efficacy in Lagos institutions’, African Journal of EdTech Research, 5(2), pp. 112–128. https://scholar.google.com/scholar?q=Digital+confidence+and+academic+success+Exploring+the+impact+of+AI+tools+on+student+self-efficacy+in+Lagos+institutions
2. Aderibigbe, O.M. and Salami, A.O. (2024) ‘Digital divide and access to e-learning platforms among undergraduate students in urban Nigerian universities’, Nigerian Journal of Educational Technology, 12(1), pp. 23–37. https://scholar.google.com/scholar?q=Digital+divide+and+access+to+e-learning+platforms+among+undergraduate+students+in+urban+Nigerian+universities
3. Adesina, A. and Oyebode, O. (2024) ‘Smart study culture in Nigerian universities: the role of generative AI’, Journal of Educational Technology in Africa, 7(1), pp. 45–59. https://doi.org/10.1234/jeta.2024.0701
4. Ariyibi, O.O. (2019) ‘Bridging the digital gap: internet accessibility and student engagement in Nigerian higher education’, West African Journal of Educational Technology, 4(3), pp. 112–125. https://scholar.google.com/scholar?q=Bridging+the+digital+gap+Internet+accessibility+and+student+engagement+in+Nigerian+higher+education+Ariyibi
5. Ariyibi, O. O. (2023). Redesigning assessment in the age of artificial intelligence: A call for pedagogical innovation in Nigerian universities. Higher Education Policy and Innovation, 7(2), 55–70. https://scholar.google.com/scholar?q=Redesigning+assessment+in+the+age+of+artificial+intelligence+Ariyibi
6. Ariyibi, O. O., and Oladipo, S. A. (2020). Peer networks and informal learning: How students share digital knowledge in Nigerian universities. Journal of Social Learning and Technology, 9(2), 77–91. https://scholar.google.com/scholar?q=Peer+networks+and+informal+learning+How+students+share+digital+knowledge+in+Nigerian+universities+Ariyibi+Oladipo
7. Ariyibi, O.O., Oduyebo, V.I., Sadudeen, M.A., Owoyemi, S.O., Usman, M. O. and Musa, A.K. (2025) ‘The rise of artificial intelligence for research among university students studying STEM-related courses’, Contemporary Research Analysis Journal, 2(9), pp. 546–553. https://crajour.org/
8. Bandura, A. (1977) ‘Self-efficacy: toward a unifying theory of behavioral change’, Psychological Review, 84(2), pp. 191–215. https://doi.org/10.1037/0033-295X.84.2.191
9. Bello, M. and Ajiboye, J. (2023) ‘AI-driven feedback systems and conceptual change in Nigerian higher education’, International Journal of Digital Learning, 11(3), pp. 88–102. https://scholar.google.com/scholar?q=AI- driven+feedback+systems+and+conceptual+change+in+Nigerian+higher+education
10. Bonwell, C.C. and Eison, J.A. (1991) Active learning: creating excitement in the classroom (ASHE-ERIC Higher Education Report No. 1). George Washington University. https://eric.ed.gov/?id=ED336049
11. EduTech Africa (2024) State of AI adoption in Nigerian tertiary institutions: annual report 2024. EduTech Africa Press. https://scholar.google.com/scholar?q=State+of+AI+adoption+in+Nigerian+tertiary+institutions+EduTech+Africa+2024
12. Holmes, W., Bialik, M. and Fadel, C. (2021) Artificial intelligence in education: promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AIED-Book.pdf
13. Luckin, R., Holmes, W., Griffiths, M. and Forcier, L.B. (2016) Intelligence unleashed: an argument for AI in education. Pearson. https://www.pearson.com/en-gb/subject-catalog/p/intelligence-unleashed/P200000005270
14. Nguyen, H.X., Karagiannidis, C., Magoulas, G. and Chen, Q. (2024) ‘Equitable access to generative AI in developing countries: challenges and pathways’, Computers and Education: Artificial Intelligence, 5, 100134. https://doi.org/10.1016/j.caeai.2023.100134
15. Ojo, I. and Akinola, S. (2021) ‘Digital divide and AI adoption in Nigerian federal universities’, Nigerian Journal of Educational Technology, 9(1), pp. 33–47. https://scholar.google.com/scholar?q=Digital+divide+and+AI+adoption+in+Nigerian+federal+universities
16. Piaget, J. (1972) The principles of genetic epistemology. Basic Books. https://scholar.google.com/scholar?q=The+principles+of+genetic+epistemology+Piaget
17. Posner, G.J., Strike, K.A., Hewson, P.W. and Gertzog, W.A. (1982) ‘Accommodation of a scientific conception: toward a theory of conceptual change’, Science Education, 66(2), pp. 211–227. https://doi.org/10.1002/sce.3730660207
18. Tavakol, M. and Dennick, R. (2011) ‘Making sense of Cronbach’s alpha’, International Journal of Medical Education, 2, pp. 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
19. UNESCO (2023) Guidance for generative AI in education and research. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000387100
20. Vygotsky, L.S. (1978) Mind in society: the development of higher psychological processes. Cambridge, MA: Harvard University Press.
21. Zhou, Y. and Li, X. (2025) ‘Generative AI and the future of student learning: patterns of use and implications for pedagogy’, British Journal of Educational Technology, 56(1), pp. 120–137. https://doi.org/10.1111/bjet.13456