Published Article

ABSTRACT

This study critically examines the roles of artificial intelligence (AI) in education, with a specific focus on its potency in transforming learning experiences among Nigerian students. AI, understood as the capability of computer systems to perform tasks that typically require human intelligence such as learning, reasoning, and decision-making, has emerged as a transformative tool across various sectors, including education. Within the Nigerian educational context, AI-driven technologies—such as machine learning, natural language processing, and intelligent tutoring systems—are increasingly being utilized to enhance personalized learning, improve teaching efficiency, and facilitate data-driven decision-making. The study situates these developments within the broader concept of education as a continuous process that promotes knowledge acquisition, skill development, and societal advancement. It further explores how AI fosters inclusive, accessible, and interactive learning environments through adaptive learning systems and automation. Key roles of AI identified in the study include personalized instruction, automation of administrative tasks, improved student engagement, and the use of predictive analytics to enhance academic performance. While acknowledging challenges such as infrastructural deficits, limited digital literacy, and policy gaps in Nigeria, the study emphasizes that strategic implementation and adequate support systems are essential for maximizing AI’s impact. The study concludes that as Nigeria continues to embrace technological advancement, a balanced and strategic approach to AI adoption will be essential to ensure that its benefits are maximized while minimizing potential risks, ultimately fostering a more inclusive, efficient, and future-ready educational system. It also recommends that educational policymakers should develop clear national frameworks and guidelines for the ethical use of AI in education, addressing concerns such as data privacy, security, and algorithmic bias.