Leveraging Generative AI for Improving Learners’ Performance in A Blended Learning System A Case Study of Open, Distance and Flexible eLearning (ODFeL) MIAPOLY Geidam, Yobe State

Abstract

The increasing adoption of e-learning and blended learning environments has transformed education delivery worldwide. However, ensuring effective learner engagement and supporting students with diverse learning paces remain critical challenges. This study explores the potential of Generative Artificial Intelligence (Generative AI) to enhance learners’ performance within a blended learning system. Using the Centre for Open, Distance and Flexible eLearning (ODFeL) of Mai Idris Alooma Polytechnic, Geidam as a case study, the research integrates a generative AI model into a Learning Management System (LMS) to monitor student learning behaviors, identify slow learners, and provide personalized feedback based on individual learning styles. The system was designed to analyze learner activity logs, apply machine learning classification, and generate adaptive study plans and counseling messages tailored to each learner’s needs. Results from the experimental phase revealed that AI-driven personalization significantly improved engagement, quiz performance, and overall learner satisfaction. The findings suggest that the integration of Generative AI into blended learning frameworks can enhance inclusivity and optimize educational outcomes. The study concludes by highlighting the importance of ethical AI deployment, instructor readiness, and continuous model refinement for sustainable adoption in higher education.