Qualitative Study on Significance and Threats of Integrating Artificial Intelligence Technology at Tertiary Level of Education in Nigeria: A Systematic Review
ABSTRACT The advent of artificial intelligence (AI) has significantly transformed various sectors, including tertiary education. This paper examines the Qualitative study on significance and threats of integrating Artificial intelligence technology at tertiary level of education in Nigeria: A Systematic Review. On the other hand, AI offers unprecedented opportunities for personalizes learning. Efficient administrative processes, and advanced research capabilities. Adaptive learning systems, intelligent tutoring and predictive analytics have potential to revolutionize the educational landscape, making learning more accessible and tailored to individual needs. However, AI also poses significant challenges and threats to education in tertiary institutions. These includes concerns over the potential for job displacement among academic staff, potentially rendering some job roles obsolete, and erosion of critical thinking skills among students due to over-reliance on automated systems. Additionally, not everyone has equal access to AI, internet, and devices, lack of AI techniques designed for a specific situation that could address the needs of a particular domain, specific learning activities, or teaching goals among others. Based on the challenges identified, the paper recommended among others that all stakeholders in education including curriculum planners and policy makers should provide a framework and guardrails on how AI should be integrated in schools to assist teachers but not to replace them, but it should be an effective learning tool that lessens the burdens of both teachers and students and offers effective learning experiences for students. The paper also recommended that there is a need to provide equal access to AI, internet, and devices to all learners at tertiary institutions. Keywords: Threats, Integrating, Artificial intelligence and Higher Education
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-04-12
- DOI
- https://doi.org/10.5281/zenodo.15201934
- Primary Topic
- Face and Expression Recognition
- Type
- article
- Field-Weighted Citation Impact
- 0.00