A Comparative Analysis of AI-Driven Personalized Learning Impact on Academic Performance and Engagement Among Nigerian Undergraduates
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Abstract
The adoption of Artificial Intelligence (AI) for personalized learning (PL) is rapidly transforming global higher education. However, institutional type often dictates resource allocation and technological infrastructure, potentially leading to disparate impacts on student outcomes. This study conducted a comparative analysis of the perceived impact of AI-driven personalized learning (AI/PL) on academic performance and academic engagement of undergraduates, measured as student-perceived outcomes, across Nigeria's tripartite higher education structure, namely federal, state, and private universities. Amidst persistent infrastructural and policy disparities in the region, the research sought to determine the relationship between AI utilization and student outcomes and assess whether institutional type significantly mediates this impact. A quantitative descriptive survey research design was employed using a stratified sample of 150 undergraduates (50 from each of the federal, state, and private universities) from Oyo State, Nigeria. Data were collected using a questionnaire, utilizing a 5-point Likert scale. Statistical analysis confirmed a positive and statistically significant correlation between AI utilization and both perceived academic performance (r=0.58, p < 0.01) and academic engagement (r=0.63, p < 0.01). Significantly, One-Way Analysis of Variance (ANOVA) revealed a highly significant difference in the perceived impact scores across the three institutional types for both academic performance (F=9.87, p < 0.001) and academic engagement (F=11.24, p < 0.001). Private universities reported the highest mean scores, followed by federal and then state institutions, demonstrating that the positive effect of AI/PL is heavily moderated by institutional resource availability and infrastructural reliability. The study concludes that AI/PL holds transformative potential, but its successful and equitable realization is contingent upon overcoming structural barriers, particularly inconsistent technological infrastructure in public institutions. It is recommended that national policy mandates robust ICT infrastructure standards and ethical governance frameworks, prioritizing reliable digital access in public institutions for equitable AI integration.
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Copyright (c) 2026 Simeon Ayoade Adedokun, Ojo Abraham Olojede, Rachel Ihunanya Adeniran, Dorcas Atinuke Adedokun (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Adedokun, S. A. (2024). The Integration of Artificial Intelligence in Nursing Research: A Framework for Enhanced Data Management and Evidence-Based Practice. From Novice to Expert: Nurturing Nursing Research Skills. LAUTECH Faculty of Nursing Sciences’ Postgraduate Seminar. https://doi.org/10.13140/RG.2.2.29746.26569
Adedokun, S. A., Adedokun, D. A., Ogunkan, S. K., & Ogunniyi, O. K. (2025). Data Privacy Dominance: An Empirical Investigation into Nigerian Postgraduate Students’ Prioritization of AI Ethical Concerns in Higher Education. International Journal of Latest Technology in Engineering Management & Applied Science, 14(10), 712–719. https://doi.org/10.51583/IJLTEMAS.2025.1410000088 DOI: https://doi.org/10.51583/IJLTEMAS.2025.1410000088
Adedokun, S. A., Adeyemo, I. A., & Adedokun, D. A. (2026). Bridging the Divide: Assessing the Awareness-to-Utilization Gap of Generative AI Tools for Personalized Learning in Nigerian Higher Education. Journal of Current Research and Studies, 3(1), 6–14. https://doi.org/10.5281/ZENODO.18153562
Adedokun, S. A., Adeyemo, I. A., Adedokun, D. A., Ogunkan, S. K., & Ogunniyi, O. K. (2026). Artificial Intelligence and the Essence of Humanity: Strategic Frameworks for Utilizing Technology and Preserving Values in an Automated Era. Journal of Science Innovation and Technology Research, 10(9), 196–213. https://doi.org/10.70382/ajsitr.v10i9.069 DOI: https://doi.org/10.70382/ajsitr.v10i9.069
Adenubi, A. O., & Samuel, N. (2024). Revolutionizing Education with Artificial Intelligence and Machine Learning: Personalization, Retention, and Resource Optimization. Kasu Journal of Computer Science, 1(2), 378–391. https://doi.org/10.47514/kjcs/2024.1.2.0015 DOI: https://doi.org/10.47514/kjcs/2024.1.2.0015
Adewale, M. D., Azeta, A., Abayomi-Alli, A., & Sambo-Magaji, A. (2024). Impact of artificial intelligence adoption on students’ academic performance in open and distance learning: A systematic literature review. Heliyon, 10(22), e40025. https://doi.org/10.1016/j.heliyon.2024.e40025 DOI: https://doi.org/10.1016/j.heliyon.2024.e40025
Adeyemi, B. T., Alanamu, T., & Obisesan, R. O. (2025). Artificial Intelligence in Nigerian Higher Education: An Analysis of Opportunities and Threats. International Journal of Multidisciplinary Research and Growth Evaluation, 6(3), 592–597.
African Union. (2024, September). ARTIFICIAL INTELLIGENCE (AI) FOR SUSTAINABLE YOUTH DEVELOPMENT IN AFRICA A POLICY BRIEF. https://au.int/en/documents/20240913/artificial-intelligence-ai-sustainable-youth-development-africa-p-o-l-i-c-y-b-r-i
Agbo, A. K., Uka, E. O., & Emeka, O. K. (2023). Adoption of Artificial Intelligence in Higher Education: A Case Study of Selected Nigerian Universities. Journal of Educational Technology and Research, 10(2), 45–60.
Ajonbadi, H. A., Olawoyin, F. S., & Adekoya, O. D. (2023). The Anathema of Digital Divide in the Nigerian Higher Education: Lessons from the Pandemic. In B. S. Nayak & K. Appleford (Eds.), Beyond the Pandemic Pedagogy of Managerialism (pp. 189–208). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40194-7_10 DOI: https://doi.org/10.1007/978-3-031-40194-7_10
Alanazi, A. A., Osman, K., & Halim, L. (2024). Effect of scaffolding strategies and guided discovery on higher-order thinking skills in physics education. Eurasia Journal of Mathematics, Science and Technology Education, 20(9), em2496. https://doi.org/10.29333/ejmste/14980 DOI: https://doi.org/10.29333/ejmste/14980
Bali, B., Garba, E. J., Ahmadu, A. S., Takwate, K. T., & Malgwi, Y. M. (2024). Analysis of emerging trends in artificial intelligence for education in Nigeria. Discover Artificial Intelligence, 4(1), 110. https://doi.org/10.1007/s44163-024-00163-y DOI: https://doi.org/10.1007/s44163-024-00163-y
Bandura, A. (1986). Social Foundations of Thought and Action. In D. Marks, The Health Psychology Reader (pp. 94–106). SAGE Publications Ltd. https://doi.org/10.4135/9781446221129.n6 DOI: https://doi.org/10.4135/9781446221129.n6
Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: Complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652–661. https://doi.org/10.1177/1744987120927206 DOI: https://doi.org/10.1177/1744987120927206
Charlotte Nickerson. (2025, March 31). Albert Bandura’s Social Cognitive Theory [Blog]. Simply Psychology. https://www.simplypsychology.org/social-cognitive-theory.html
Ebekozien, A., Samsurijan, M. S., Aigbavboa, C., Radin Firdaus, R. B., Nor Azazi, N. A., & Amadi, G. C. (2023). Infrastructure development in higher institutions: The role of private organisations via unexplored expanded corporate social responsibility (ECSR). Property Management, 41(1), 149–168. https://doi.org/10.1108/PM-03-2022-0015 DOI: https://doi.org/10.1108/PM-03-2022-0015
Egele, V. S., Klopp, E., & Stark, R. (2025). How Valid Is Bandura’s Social Cognitive Theory to Explain Physical Activity Behavior? European Journal of Investigation in Health, Psychology and Education, 15(2), 20. https://doi.org/10.3390/ejihpe15020020 DOI: https://doi.org/10.3390/ejihpe15020020
Ejeh, P. C., & Igbokwe, A. C. (2025). ARTIFICIAL INTELLIGENCE AND THE FUTURE OF NIGERIAN UNIVERSITY STUDENTS. OCHENDO: An African Journal of Innovative Studies, 6(1). https://acjol.org/index.php/ochendo/article/view/6685
Elenode, A. (2026). Adoption of Generative AI and Learning Analytics for Personalized Learning in Nigerian Higher Education: Effects on Student Engagement, Learning Outcomes, and Academic Integrity. Engineering and Applied Sciences, 11(1), 1–5. https://doi.org/10.11648/j.eas.20261101.11 DOI: https://doi.org/10.11648/j.eas.20261101.11
Eltahir, M. E., & Babiker, F. M. E. (2024). The Influence of Artificial Intelligence Tools on Student Performance in e-Learning Environments: Case Study. Electronic Journal of E-Learning, 22(9), 91–110. https://doi.org/10.34190/ejel.22.9.3639 DOI: https://doi.org/10.34190/ejel.22.9.3639
Eneye, A. M., Oziohu, A. M., & Abraham, E. O. (2025). The Impact of Generative AI in Personalized Learning on Students Learning Outcomes; Teachers Perception of Impact of AI in the 21st Century. Babcock University Journal of Education, 10(2), 3–15. https://doi.org/10.5281/zenodo.15716158
Ezekiel, O. B., & Akinyemi, A. L. (2022). UTILISATION OF ARTIFICIAL INTELLIGENCE IN EDUCATION: THE PERCEPTION OF UNIVERSITY OF IBADAN LECTURERS. Journal of Global Research in Education and Social Science, 32–40. https://doi.org/10.56557/jogress/2022/v16i58053 DOI: https://doi.org/10.56557/jogress/2022/v16i58053
Hashim, S., Omar, M. K., Ab Jalil, H., & Mohd Sharef, N. (2022). Trends on Technologies and Artificial Intelligence in Education for Personalized Learning: Systematic Literature Review. International Journal of Academic Research in Progressive Education and Development, 11(1), Pages 884-903. https://doi.org/10.6007/IJARPED/v11-i1/12230 DOI: https://doi.org/10.6007/IJARPED/v11-i1/12230
Idika, S., & Saihi, S. (2025). Enhancing Student Engagement through AI-Driven Adaptive Learning and Gamification. British Journal of Education, 13(12), 57–103. https://doi.org/10.37745/bje.2013/vol13n1257103 DOI: https://doi.org/10.37745/bje.2013/vol13n1257103
Kamil, A., Shah, D. F. ul H., Atif, S., Hayat, S., Aziz, S., & Nisar, A. (2025). ROLE OF ARTIFICIAL INTELLIGENCE IN PERSONALIZED LEARNING. Contemporary Journal of Social Science Review, 3(3), 667–680. https://doi.org/10.63878/cjssr.v3i3.1011 DOI: https://doi.org/10.63878/cjssr.v3i3.1011
Lu, G., Xie, K., & Liu, Q. (2022). What influences student situational engagement in smart classrooms: Perception of the learning environment and students’ motivation. British Journal of Educational Technology, 53(6), 1665–1687. https://doi.org/10.1111/bjet.13204 DOI: https://doi.org/10.1111/bjet.13204
Mathew, S., & Isaac, E. A. (2025). Artificial Intelligence (AI): A Threat to Education in Nigeria. Journal of Education Research and Library Practice, 8(8). https://doi.org/10.70382/10.70382/ajerlp.v8i8.027 DOI: https://doi.org/10.70382/10.70382/ajerlp.v8i8.027
Nwodu, G. E. (2025). Awareness and Perception of the Use of Artificial Intelligence for Learning Among Select Communication Undergraduates in Nigeria. African Journal of Social Sciences and Humanities Research, 8(1), 113–130. https://doi.org/10.52589/ajsshr-qke2a0eg DOI: https://doi.org/10.52589/AJSSHR-QKE2A0EG
OECD. (2021). OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. OECD Publishing. https://doi.org/10.1787/589b283f-en DOI: https://doi.org/10.1787/589b283f-en
Okolie, U. C., & Egbon, T. Nkech. (2025). ARTIFICIAL INTELLIGENCE AND TEACHINGLEARNING PROCESS IN EDUCATION INSTITUTIONS IN NIGERIA: A STUDY OF UNDERGRADUATE STUDENTS OF DELTA STATE UNIVERSITY, ABRAKA. Journal Plus Education, 37(1), 122–136. https://doi.org/10.24250/jpe/1/2025/UC/TNE/ DOI: https://doi.org/10.24250/jpe/1/2025/UC/TNE/
Pratama, Muh. P., Sampelolo, R., & Lura, H. (2023). REVOLUTIONIZING EDUCATION: HARNESSING THE POWER OF ARTIFICIAL INTELLIGENCE FOR PERSONALIZED LEARNING. KLASIKAL: JOURNAL OF EDUCATION, LANGUAGE TEACHING AND SCIENCE, 5(2), 350–357. https://doi.org/10.52208/klasikal.v5i2.877 DOI: https://doi.org/10.52208/klasikal.v5i2.877
Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson.
Sanusi, I. (2025). Artificial Intelligence in School Education: The Case of Nigeria. Revue Internationale d’éducation de Sèvres, HS-4. https://doi.org/10.4000/146vp DOI: https://doi.org/10.4000/146vp
Sanusi, O. T., Adebayo, L. A., & Bamidele, M. O. (2024). The Use of Artificial Intelligence by Undergraduates in Nigerian Universities for Academic Purposes. Nigerian Journal of Educational Technology, 12(1), 78–92.
Schunk, D. H., & DiBenedetto, M. K. (2023). Learning from a social cognitive theory perspective. In International Encyclopedia of Education (Fourth Edition) (pp. 22–35). Elsevier. https://doi.org/10.1016/B978-0-12-818630-5.14004-7 DOI: https://doi.org/10.1016/B978-0-12-818630-5.14004-7
Souza, A. P. D. S., Conceição, C. D. J., Pancoto, M. A., Cecote, N. Q. B., Pedra, R. R., Oliveira, R. M. D. S., Pinão, V. R. Z., & Gomes, W. T. (2024). PERSONALIZATION OF LEARNING WITH ARTIFICIAL INTELLIGENCE: HOW AI IS TRANSFORMING EDUCATION AND CURRICULUM. ARACÊ, 6(3), 5816–5831. https://doi.org/10.56238/arev6n3-092 DOI: https://doi.org/10.56238/arev6n3-092
Tapalova, O., & Zhiyenbayeva, N. (2022). Artificial Intelligence in Education: AIEd for Personalised Learning Pathways. Electronic Journal of E-Learning, 20(5), 639–653. https://doi.org/10.34190/ejel.20.5.2597 DOI: https://doi.org/10.34190/ejel.20.5.2597
UNESCO. (2025, September 15). Ethics of Artificial Intelligence. https://www.unesco.org/en/artificial-intelligence/recommendation-ethics
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167 DOI: https://doi.org/10.1016/j.eswa.2024.124167
Yang, X., Liu, X., & Gao, Y. (2025). The impact of Generative AI on students’ learning: A study of learning satisfaction, self-efficacy and learning outcomes. Educational Technology Research and Development. https://doi.org/10.1007/s11423-025-10540-8 DOI: https://doi.org/10.1007/s11423-025-10540-8
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0 DOI: https://doi.org/10.1186/s41239-019-0171-0
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