The Impact of AI-Driven Real-Time Feedback Systems on Students’Self-Regulated Learning and Academic Persistence in Secondary Schools in Nigeria

Abstract

Self-regulated learning (SRL) is essential for secondary school students to achieve academic success and lifelong learning competencies, particularly in contexts requiring greater learner autonomy. This expository article examines the potential of Artificial Intelligence (AI)-driven real-time feedback systems to support SRL processes planning (forethought), monitoring (performance/control), and reflection within Nigerian secondary education. Grounded in Zimmerman’s cyclical SRL model and Bandura’s Social Cognitive Theory, the paper conceptualises AI tools (e.g., intelligent tutoring systems, learning analytics dashboards, chatbots, and adaptive feedback platforms) as environmental scaffolds that enhance metacognition, motivation, self-efficacy, and behavioural regulation. Drawing on global and emerging empirical evidence, AI facilitates personalised, timely feedback that targets process- and self-regulation levels, promoting deeper engagement, strategy adaptation, and persistence. However, implementation in Nigeria faces significant challenges, including infrastructural deficits (e.g., unreliable internet and power), limited digital literacy, teacher readiness, unequal access, and ethical concerns such as data privacy, bias, and over-reliance, leading to diminished learner agency. The conceptual framework positions AI as a mediator of SRL dimensions while moderated by contextual and ethical factors. Empirical reviews indicate positive associations between AI-supported feedback and improved SRL behaviours, though longitudinal studies in developing contexts remain scarce. The article underscores the need for balanced, human-centred integration to foster genuine autonomy rather than dependency. It concludes with practical recommendations for policymakers, educators, and developers to advance equitable AI adoption in resource-constrained secondary settings.