Impact of Machine Learning on Personalized Learning and Course Customization

The integration of Machine Learning (ML) into education is transforming traditional learning environments by enabling personalized, data-driven instructional methods. As conventional models increasingly fall short in addressing diverse student needs, ML technologies offer adaptive learning solutions that tailor content, pace, and teaching strategies to individual learners. In this paper, we investigate the application of ML in education. Although global progress has been notable, specific challenges persist in regions such as the Gulf Cooperation Council, including concerns about data privacy, infrastructure limitations, resistance to pedagogical change, and the need for culturally localized AI tools. Through a mixed-method research design, this study evaluates the effectiveness of a ML-based personalized learning system compared to traditional approaches. Surveys, academic performance metrics, and teacher observations are analyzed to assess outcomes related to student engagement and satisfaction.