Artificial intelligence, academic staff job performance and students’ academic performance in universities in Nigeria
Year: 2025
Type: article
Abstract: This paper explores the transformative impact of artificial intelligence (AI) on academic staff job performance and students’ academic achievement in Nigerian universities. As a review study, the research relies exclusively on secondary data obtained from a wide array of print and online sources, including peer-reviewed journals, institutional reports, and policy documents. The findings demonstrate that AI technologies, including intelligent tutoring systems, automated grading tools, and AI-powered research platforms, significantly improve the productivity, efficiency, and instructional quality of academic staff. Simultaneously, AI-driven learning tools, personalized content delivery, and adaptive assessment models have enhanced student engagement, learning outcomes, and academic performance. The paper also identifies the growing integration of AI in curriculum delivery, administrative support, and educational decision-making, thereby reinforcing the digital transformation of tertiary education in Nigeria. However, the study acknowledges the challenges of AI adoption, including inadequate digital infrastructure, low technological literacy, and limited policy frameworks guiding ethical AI usage. In response to these findings, the paper recommends strategic investments by both government and private sector actors in AI infrastructure across tertiary institutions. It also underscores the urgent need for targeted training programs to enhance the AI literacy of both lecturers and students, thereby maximizing the potential benefits of AI while ensuring equitable access and responsible usage. Ultimately, the study argues that embracing AI is crucial for positioning Nigerian universities as competitive, innovative, and inclusive institutions in the global knowledge economy.
Author: Ukozor, Conrad Ugochukwu
Institution: University of Abuja
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7220343789