International Journal of Science, Engineering and TechnologyAuthors: Azmi, Ayush Vashistha, Chouhan Kumar Rath
Abstract: Online learning platforms have expanded rapidly in recent years; however, most existing systems still follow a uniform content delivery approach that fails to address individ ual learning differ-ences. Variations in learning pace, topic-wise understanding, and response behavior often result in reduced en gagement and ineffective learning outcomes. This paper presents an AI-based adaptive learning framework that personalizes educational resources by analyzing learner assessment data. The system integrates unsupervised clustering and supervised classification methods to catego-rize learners into three proficiency levels—Beginner, Intermediate, and Advanced—using features such as accuracy, response time, topic-wise performance, attempt frequency, and improvement rate. Ensemble learning techniques are employed to improve the accuracy of predictions and cap-ture complex learning patterns. Based on predicted proficiency levels and identified weak areas, a hybrid recommendation mechanism dynamically adjusts question difficulty and provides personal ized learning materials, including instructional videos, reading content, and adaptive practice ex-ercises. Experimental evalu ation using standard classification metrics indicates increased learner engagement and measurable performance improvement following adaptive recommendations. The proposed framework offers a scalable and data-driven solution for personalized online education through constant observation and adaptive updates to support effective knowledge acquisition and long-term retention.
DOI: http://doi.org/10.5281/zenodo.21701942
