An Overview of Supervised Learning Techniques in Contemporary Educational Technologies

The rapid advancement of artificial intelligence has catalyzed significant transformations in educational technology, enabling more personalized, adaptive, and data-driven approaches to teaching and learning. The paper examines the theoretical foundations and practical applications of various supervised learning algorithms. The analysis reveals that these techniques offer powerful tools for predicting student performance, automating assessment processes, identifying at-risk students, and personalizing learning. However, their implementation faces challenges related to data quality, model interpretability, and algorithmic bias. The review contributes to the growing body of knowledge on AI in education by synthesizing current research, identifying implementation challenges, and proposing pathways for responsible and effective integration of supervised learning techniques in educational contexts.