ARTIFICIAL INTELLIGENCE AND PREDICTIVE ANALYTICS IN STUDENT PERFORMANCE MONITORING: A PLANNING PERSPECTIVE IN NORTH-EAST NIGERIAN UNIVERSITIES

Abstract

Artificial Intelligence (AI) and predictive analytics are reshaping educational monitoring systems globally by enabling universities to forecast academic outcomes and guide strategic student support. In North-East Nigeria, where insecurity, infrastructural gaps, & administrative strain continue to affect tertiary education, predictive systems offer opportunities to enhance early-warning interventions, strengthen institutional planning, and support data-driven student management. This study investigated the use of AI-enabled performance prediction tools among five public universities in the region, focusing on their effectiveness in improving decision-making and student outcome planning. A descriptive research design was applied, with data collected from 350 academic administrators and ICT personnel through a structured questionnaire. Results indicated a moderate level of AI adoption and significant influence of predictive analytics on academic monitoring processes. Findings revealed that universities utilizing AI systems recorded improved identification of at-risk students, better academic advisory planning, and more efficient resource allocation. Nonetheless, challenges such as limited technical skills, infrastructure deficits, and ethical concerns regarding data governance were evident. The study recommends capacity building for staff, investment in digital infrastructure, institutional AI policies, and ethical data standards to maximize the benefits of AI-driven student monitoring systems. The conclusions emphasize that while AI holds strong potential for academic transformation, strategic planning remains essential for equitable and sustainable implementation in the regional higher-education system. AI-enabled predictive analytics significantly support student performance monitoring and academic planning in North-East Nigeria. Universities using predictive tools achieve earlier intervention, improved resource allocation, and enhanced academic outcomes. Strengthening staff capacity, infrastructure, and policy frameworks is necessary to sustain effective adoption and maximize student-success benefits.