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Navigating the complexity of generative artificial intelligence in higher education: a systematic literature review.

Amofa, Birago; Kamudyariwa, Xebiso Blessing; Fernandes, Fatima Araujo Pereira; Osobajo, Oluyomi Abayomi; Jeremiah, Faith; Oke, Adekunle

Authors

Fatima Araujo Pereira Fernandes

Faith Jeremiah

Adekunle Oke



Abstract

Technological innovation has transformed educational settings, enabling artificial intelligence (AI)-driven teaching and learning processes. While AI is still in its embryonic stage in education, generative artificial intelligence has evolved rapidly, significantly shifting the teaching and learning context. With no clarity about the impacts of generative artificial intelligence on education, there is a need to synthesise research findings to demystify generative artificial intelligence and address concerns regarding its application in the teaching and learning process. This paper systematically synthesises studies on generative artificial intelligence in teaching and learning to understand key arguments and stakeholders’ perceptions of generative artificial intelligence in teaching and learning. The systematic review reveals five main domains of research within the field: (i) current awareness (understanding) of generative artificial intelligence, (ii) stakeholder perceptions, (iii) mechanisms for adopting generative artificial intelligence, (iv) issues and challenges of implementing generative artificial intelligence, and (v) contributions of generative artificial intelligence to student performance. This review examines the practical and policy implications of generative artificial intelligence, providing recommendations to address the concerns and challenges associated with generative artificial intelligence-driven teaching and learning processes.

Citation

AMOFA, B., KAMUDYARIWA, X.B., FERNANDES, F.A.P., OSOBAJO, O.A., JEREMIAH, F. and OKE, A. 2025. Navigating the complexity of generative artificial intelligence in higher education: a systematic literature review. Education sciences [online], 15(7), article number 826. Available from: https://doi.org/10.3390/educsci15070826

Journal Article Type Article
Acceptance Date Jun 22, 2025
Online Publication Date Jun 29, 2025
Publication Date Jul 31, 2025
Deposit Date Aug 1, 2025
Publicly Available Date Aug 1, 2025
Journal Education sciences
Electronic ISSN 2227-7102
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 15
Issue 7
Article Number 826
DOI https://doi.org/10.3390/educsci15070826
Keywords Student performance; Ethical issues; Teaching and learning; Generative artificial intelligence; Technological innovation
Public URL https://rgu-repository.worktribe.com/output/2928773

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