Artificial Intelligence and Machine Learning in Education: A Systematic Literature Review of Transformative Trends and Future Directions - Aliyah Aliyah (PT. Faaslib Serambi Media, 2026)
The transformation of education in the digital era has been significantly accelerated by the integration of Artificial Intelligence (AI) and Machine Learning (ML), fundamentally reshaping how learning is designed, delivered, and assessed. This study aims to systematically identify emerging trends, key benefits, prevailing challenges, and future directions of AI and ML applications in education through a Systematic Literature Review (SLR) approach. The reviewed literature was sourced from leading academic databases, including Scopus, IEEE Xplore, and ScienceDirect, covering publications from 2015 to 2025. The findings reveal that AI and ML technologies have been widely implemented in various educational domains, particularly in adaptive learning systems, automated assessment mechanisms, and intelligent virtual assistants that facilitate personalized learning experiences. Despite these advancements, several critical challenges persist, notably digital inequality, data privacy concerns, and the limited technological literacy among educators, which hinder the effective adoption of these technologies. Furthermore, the study highlights that the future of education will increasingly rely on the integration of intelligent systems that enable data-driven, flexible, and learner-centered environments. The insights derived from this SLR are expected to provide valuable guidance for policymakers, educators, and technology developers in formulating adaptive and sustainable educational strategies in the era of artificial intelligence.