A Strategic Design of Personalized Based Learning System for Improving the Experience of Outcome Based Education

With changing dynamics in higher education, methodologies driven by data, and the integration of artificial intelligence (AI) are emerging as perhaps the most transformative forces that are sure to overhaul traditional models of teaching and learning. This paper looks into the rapidly rising discipline of data-driven education and how its fostering factors could bring their contributions to helping realize the proposed vision for a new future of personalized learning experiences within higher learning institutions. Therefore, the general objective of the study is to establish how integrated AI technologies within the educational data analytics system are poised to support the heterogeneous needs of learners, boost teaching modalities, and eventually improve academic gains. In fact, large datasets of students’ demographic, learning preferences, performance, and academic datasets will offer deep insights into individual learning trajectories, which, in turn, will be the basis for tailoring educational interventions. The research is much relevant to the study that all the researched approached, including machine learning algorithms, techniques of natural language processing, and predictive analytic models, be studied. It allows educational stakeholders to observe patterns in such data and thus predict student behavior for the prescription of personalized learning pathways. This enabled students to access personalized content, receive continuous feedback, and at the same time have an interactive learning experience that was tailored to their level of proficiency and style of learning.