Applications of Artificial Intelligence in Education: A Review of Learning Analytics and Open Educational Data Sources.

Abstract

Artificial Intelligence (AI) is quickly transforming the educational world, enabling personal, scalable and data-driven learning systems. The third element of AI technologies, learning analytics, and open educational sources of data, has opened new opportunities to enhance the effectiveness of teaching and student performance. Learning analytics deems relevance in deriving meaningful significance out of large volumes of educational information in support of activities such as predicting student performance, tracking engagement and early intervention. Meanwhile, open educational data sources, such as MOOCs, open educational resources, and institutional repositories, offer the required basis to train and optimize AI-driven models. Combining these aspects enables the development of adaptive learning systems, intelligent tutoring systems, and recommendation engines to tailor educational experiences to the needs of individual learners. Despite these attempts, there are still challenges such as data privacy, algorithmic bias, interoperability, and unequal access to technology that hinder the full potential of AI in education. These are key points that must be tackled to achieve ethical, transparent and inclusive implementation. Overall, the merging of AI, learning analytics, and open data is a major move towards more efficient, fair, and student-centered systems of education.