This study examines the integration of generative artificial intelligence (GenAI) and learning analytics (LA) in fostering personalized learning within Nigerian higher education institutions. Specifically, it explores how these emerging technologies influence student engagement, academic performance, and perceptions of academic integrity in digitally mediated learning environments. A mixed-method research design was adopted, involving 420 undergraduate students and 60 academic staff drawn from three Nigerian universities. Quantitative data were collected through structured questionnaires assessing GenAI usage patterns, learning analytics engagement, levels of digital literacy, and integrity perceptions, while qualitative insights were obtained from semi-structured interviews with academic staff and information and communication technology (ICT) administrators. The findings reveal that 64% of respondents actively use GenAI tools such as ChatGPT and Gemini to support learning activities, while 58% regularly access learning analytics dashboards for performance monitoring and feedback. Regression analysis indicates that GenAI-assisted learning and engagement with learning analytics jointly predict improved academic performance (β = 0.36, p < 0.01) and higher levels of student engagement (β = 0.42, p < 0.001). However, the study also finds that unregulated use of GenAI tools is positively associated with increased concerns about plagiarism and academic misconduct (r = 0.31, p < 0.05). The study concludes that the pedagogically guided integration of GenAI and learning analytics can significantly enhance personalized learning experiences in Nigerian higher education. Nevertheless, this potential can only be realized through the development of robust governance frameworks that emphasize ethical use, digital literacy, academic integrity, and data privacy. The study therefore recommends targeted capacity-building for educators, the establishment of transparent institutional AI policies, and continuous evaluation of the pedagogical impact of GenAI technologies.
| Published in | Engineering and Applied Sciences (Volume 11, Issue 1) |
| DOI | 10.11648/j.eas.20261101.11 |
| Page(s) | 1-5 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
Generative Artificial Intelligence (GenAI), Learning Analytics, Personalized Learning, Academic Integrity
Purpose | Frequent (%) | Occasional (%) | Rare (%) | Mean (SD) |
|---|---|---|---|---|
Assignment assistance | 59.8 | 28.1 | 12.1 | 4.10 (0.73) |
Concept clarification | 63.5 | 25.0 | 11.5 | 4.24 (0.68) |
Exam preparation | 61.0 | 27.4 | 11.6 | 4.21 (0.65) |
Research writing | 48.9 | 31.0 | 20.1 | 3.81 (0.82) |
Engagement Level | High (%) | Moderate (%) | Low (%) | Mean GPA (SD) |
|---|---|---|---|---|
Dashboard use | 57.6 | 29.0 | 13.4 | 3.72 (0.48) |
Instructor feedback | 60.2 | 27.1 | 12.7 | 3.77 (0.42) |
Peer comparison | 52.5 | 33.2 | 14.3 | 3.69 (0.50) |
Variable | GenAI Use | Engagement | Integrity |
|---|---|---|---|
GenAI Use | 1.00 | ||
Engagement | 0.42** | 1.00 | |
Integrity | -0.31* | 0.18 | 1.00 |
AI | Artificial Intelligence |
GenAI | Generative Artificial Intelligence |
LA | Learning Analytics |
ICT | Information and Communication Technology |
GPA | Grade Point Average |
ALAIS | AI and Learning Analytics Integration Scale |
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APA Style
Elenode, A. W. (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
ACS Style
Elenode, A. W. Adoption of Generative AI and Learning Analytics for Personalized Learning in Nigerian Higher Education: Effects on Student Engagement, Learning Outcomes, and Academic Integrity. Eng. Appl. Sci. 2026, 11(1), 1-5. doi: 10.11648/j.eas.20261101.11
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Download@article{10.11648/j.eas.20261101.11,
author = {Ayonote Williams Elenode},
title = {Adoption of Generative AI and Learning Analytics for Personalized Learning in Nigerian Higher Education: Effects on Student Engagement, Learning Outcomes, and Academic Integrity},
journal = {Engineering and Applied Sciences},
volume = {11},
number = {1},
pages = {1-5},
doi = {10.11648/j.eas.20261101.11},
url = {https://doi.org/10.11648/j.eas.20261101.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20261101.11},
abstract = {This study examines the integration of generative artificial intelligence (GenAI) and learning analytics (LA) in fostering personalized learning within Nigerian higher education institutions. Specifically, it explores how these emerging technologies influence student engagement, academic performance, and perceptions of academic integrity in digitally mediated learning environments. A mixed-method research design was adopted, involving 420 undergraduate students and 60 academic staff drawn from three Nigerian universities. Quantitative data were collected through structured questionnaires assessing GenAI usage patterns, learning analytics engagement, levels of digital literacy, and integrity perceptions, while qualitative insights were obtained from semi-structured interviews with academic staff and information and communication technology (ICT) administrators. The findings reveal that 64% of respondents actively use GenAI tools such as ChatGPT and Gemini to support learning activities, while 58% regularly access learning analytics dashboards for performance monitoring and feedback. Regression analysis indicates that GenAI-assisted learning and engagement with learning analytics jointly predict improved academic performance (β = 0.36, p < 0.01) and higher levels of student engagement (β = 0.42, p < 0.001). However, the study also finds that unregulated use of GenAI tools is positively associated with increased concerns about plagiarism and academic misconduct (r = 0.31, p < 0.05). The study concludes that the pedagogically guided integration of GenAI and learning analytics can significantly enhance personalized learning experiences in Nigerian higher education. Nevertheless, this potential can only be realized through the development of robust governance frameworks that emphasize ethical use, digital literacy, academic integrity, and data privacy. The study therefore recommends targeted capacity-building for educators, the establishment of transparent institutional AI policies, and continuous evaluation of the pedagogical impact of GenAI technologies.},
year = {2026}
}
TY - JOUR T1 - Adoption of Generative AI and Learning Analytics for Personalized Learning in Nigerian Higher Education: Effects on Student Engagement, Learning Outcomes, and Academic Integrity AU - Ayonote Williams Elenode Y1 - 2026/01/16 PY - 2026 N1 - https://doi.org/10.11648/j.eas.20261101.11 DO - 10.11648/j.eas.20261101.11 T2 - Engineering and Applied Sciences JF - Engineering and Applied Sciences JO - Engineering and Applied Sciences SP - 1 EP - 5 PB - Science Publishing Group SN - 2575-1468 UR - https://doi.org/10.11648/j.eas.20261101.11 AB - This study examines the integration of generative artificial intelligence (GenAI) and learning analytics (LA) in fostering personalized learning within Nigerian higher education institutions. Specifically, it explores how these emerging technologies influence student engagement, academic performance, and perceptions of academic integrity in digitally mediated learning environments. A mixed-method research design was adopted, involving 420 undergraduate students and 60 academic staff drawn from three Nigerian universities. Quantitative data were collected through structured questionnaires assessing GenAI usage patterns, learning analytics engagement, levels of digital literacy, and integrity perceptions, while qualitative insights were obtained from semi-structured interviews with academic staff and information and communication technology (ICT) administrators. The findings reveal that 64% of respondents actively use GenAI tools such as ChatGPT and Gemini to support learning activities, while 58% regularly access learning analytics dashboards for performance monitoring and feedback. Regression analysis indicates that GenAI-assisted learning and engagement with learning analytics jointly predict improved academic performance (β = 0.36, p < 0.01) and higher levels of student engagement (β = 0.42, p < 0.001). However, the study also finds that unregulated use of GenAI tools is positively associated with increased concerns about plagiarism and academic misconduct (r = 0.31, p < 0.05). The study concludes that the pedagogically guided integration of GenAI and learning analytics can significantly enhance personalized learning experiences in Nigerian higher education. Nevertheless, this potential can only be realized through the development of robust governance frameworks that emphasize ethical use, digital literacy, academic integrity, and data privacy. The study therefore recommends targeted capacity-building for educators, the establishment of transparent institutional AI policies, and continuous evaluation of the pedagogical impact of GenAI technologies. VL - 11 IS - 1 ER -
Directorate of Information and Communication Technology, National Open University of Nigeria, Abuja, Nigeria
Biography: Ayonote Williams Elenode, FIPMA, MCPN, MNCS, is a seasoned ICT executive and Chartered Information Technology Practitioner with over 21 years of experience in digital infrastructure management, educational technology, and ICT-in-education strategy. He serves as Deputy Director (ICT) at the National Open University of Nigeria (NOUN), where he leads digital transformation initiatives, data analytics, and enterprise ICT services supporting over 150,000 active learners. He holds an M.IT. in Information Technology, an M.Ed. in Educational Technology, an LL.B. in Law, a B.Sc. and an ND in Computer Science, as well as a professional diploma in Data Processing and a certificate in Entrepreneurship. He is currently completing a PhD in Science Education (ICT in Education). He is a Fellow of the Institute of Professional Managers and Administrators of Nigeria and a member of the Computer Professionals Registration Council of Nigeria and the Nigeria Computer Society.
Research Fields: ICT applications in education, assessment and examination malpractice control, science education, technology-driven innovation, and learning analytics.
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