Human-Centered Machine Learning for Educational Systems: A Review of Prediction, Behavioral Analytics, and Explainable AI

Abstract: Machine learning (ML) has become a fundamental component of modern educational systems [1], [2]. Its applications extend from predictive analytics to learning analytics, personalization, and decision support [3]-[5]. This paper presents a comprehensive and human-centered review of ML applications in education using recent studies [1]- [10]. The study emphasizes interpretability, pedagogy, and ethical considerations. It identifies key application domains, commonly used techniques, and emerging research trends. Challenges such as bias, transparency, and scalability are critically examined [5], [10]. A structured literature review table and detailed discussion are provided. The paper concludes with research gaps and future directions for responsible and inclusive ML integration in education.

Keywords: Machine Learning in Education, Explainable AI, Learning Analytics, Personalized Learning, Educational Data Mining