ARTIFICIAL INTELLIGENCE IN EDUCATION: ENHANCING LEARNING OUTCOMES THROUGH PREDICTIVE ANALYTICS

Abstract

AI and predictive analytics are changing the education system by providing insights that are based on data and improve learning performance and personalized teaching. The present paper will discuss the role of AI-based predictive models in detecting learning patterns, predicting academic performance and helping form an early intervention plan in various learning scenarios. It is based on recent empirical and theoretical research and is aimed at evaluating how machine learning and data mining methods can maximize the teaching efficiency, student engagement and accuracy of assessment. Another aspect of the study that addresses ethical implications is the aspect of data privacy, algorithmic bias and transparency, where being innovative and accountable balance with each other. Finally, the paper suggests that responsible predictive analytics can be an innovative methodology foundational tool to bridge the realms of educational psychology, data science, and institution design to improve learning outcomes and institutional decision-making in higher education.