Paper Details

Name: Parkhi Acchreja, Adith M.R., Abhay Kejriwal, Narinder Yadav & Akarshan Jangid
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 384-393
DOI: https://doi.org/10.5281/zenodo.19017804

ABSTRACT
Self-education undergoes a transformation through Machine Learning because it supports teachers to build and enhance customized educational activities which align with student-specific needs. Through analysis of student interaction data ML discovers methods which boost student commitment together with comprehension and educational success outcomes. The implementation of adaptive learning systems and predictive analytics for intervention and automated feedback and material recommendations represent some key instances of ML usage. The educational benefits of ML remain challenging by three main factors: privacy concerns along with discriminatory practices and difficulties scaling algorithmic capabilities. The responsible application of AI systems must remain a priority because improper ethical choices and irregular fair learning implementation need to be prevented. A document investigates how ML works in education by analyzing its benefits and barriers as well as potential solutions for ethical AI implementation in educational systems. This research examines how ML powers personalized education while studying its advantages and obstacles together with expected trends while stressing the requirement of ethical implementation and continuous technological advancement in education systems that leverage artificial intelligence.

General Terms
Algorithms, artificial intelligence, human factors, security, performance, design, experiment.

Keywords
Personalized learning and machine learning, adaptive education, intelligent tutoring, data privacy, AI in education, reinforcement learning, federated learning.