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Modelling the Integration of Generative AI in African Higher Education Institutions Through Mobile Learning


Authors : John G. Chaka; Habila B. Dekom

Volume/Issue : Volume 11 - 2026, Issue 7 - July


Google Scholar : https://tinyurl.com/ye2yj39h

DOI : https://doi.org/10.38124/ijisrt/26jul394

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : Although the application of artificial intelligence (AI), specifically generative AI (GenAI) may be revolutionizing all industries, particularly the education industry, its integration in developing countries might not be without other challenges. In addition to mixed reactions by stakeholders due to fears of promoting bad practices such as plagiarism, other challenges such as non-affordability, non-availability, and inadequate access to supporting hardware technology cannot be put aside. This study is part of a broader study which leverages on the availability, accessibility, affordability, and the usability of mobile devices to explore the integration of Gen AI in Higher Educations institutions in Africa, especially Nigeria. The first component of the study ascertains the perceptions of stakeholders and the factors that can affect their acceptance to integrate GenAI in facilitating pedagogical practices in African HEIs. A mixed methods research approach, which combines the quantitative and qualitative approaches is adopted in order to triangulate the results. Quantitative data was analyzed using arithmetic means, and regression analysis, while qualitative data was analyzed using content analysis. Results indicate mixed reactions especially with respect to social influence and mobile learning conditions.

References :

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  2. Arvind, S., & Atul, S. (2024). Revolutionary Applications of Generative AI in Higher Education Institutes. Library Philosophy and Practice, 1-9.
  3. Chaka, J. G., & Govender, I. (2017). Staff Acceptance of Mobile Learning: The Case of Colleges of Education in Nigeria. Journal for New Generation Sciences, 15(1), 224-242.
  4. Chan, C. K., & Colloton, T. (2024). Generative AI in Higher Education: the ChatGPT Effect. London and New York: Routledge Taylor and Francis Group.
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  11. Sukubu, T. A., & Atteng, C. J. (2023). Artificial Intelligence (AI), Curriculum, and Special Needs Education in Nigeria:. Rivers State University Faculty of Education Conference Journal, 3(1), 1-15.
  12. 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, 19(1). doi:ttps://doi.org/10.1186/s41239-019-0171-0.

Although the application of artificial intelligence (AI), specifically generative AI (GenAI) may be revolutionizing all industries, particularly the education industry, its integration in developing countries might not be without other challenges. In addition to mixed reactions by stakeholders due to fears of promoting bad practices such as plagiarism, other challenges such as non-affordability, non-availability, and inadequate access to supporting hardware technology cannot be put aside. This study is part of a broader study which leverages on the availability, accessibility, affordability, and the usability of mobile devices to explore the integration of Gen AI in Higher Educations institutions in Africa, especially Nigeria. The first component of the study ascertains the perceptions of stakeholders and the factors that can affect their acceptance to integrate GenAI in facilitating pedagogical practices in African HEIs. A mixed methods research approach, which combines the quantitative and qualitative approaches is adopted in order to triangulate the results. Quantitative data was analyzed using arithmetic means, and regression analysis, while qualitative data was analyzed using content analysis. Results indicate mixed reactions especially with respect to social influence and mobile learning conditions.

Paper Submission Last Date
30 - September - 2026

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