Vol. 8, Special Issue 4 (2019)

Personalized learning, a cornerstone of ML in education, involves the customization of educational content and delivery to meet the unique needs and preferences of each student. ML algorithms analyze vast datasets to discern patterns in individual learning styles, strengths, and weaknesses, enabling the creation of adaptive learning pathways. By dynamically adjusting the pace, content, and difficulty of lessons, personalized learning not only accommodates diverse learning preferences but also optimizes knowledge retention and student engagement.

Educational technology, when coupled with ML, amplifies the impact of personalized learning. Intelligent tutoring systems, recommendation engines, and virtual learning environments harness ML algorithms to provide targeted feedback, suggest relevant learning materials, and create immersive, interactive learning experiences. As a result, students benefit from a more responsive and tailored educational journey that aligns with their unique cognitive processes.

This paper also delves into the ethical considerations surrounding ML in education, emphasizing the importance of data privacy, transparency, and equity. Striking a balance between the advantages of personalized learning and safeguarding sensitive student information is crucial for the responsible implementation of ML in educational settings.