<![CDATA[In the past decade, the global education landscape has undergone a fundamental transformation along with the massive penetration of Artificial Intelligence (AI) technology. This shift is marked by a transition from conventional teaching methods to a sophisticated adaptive learning ecosystem, where technologies such as machine learning, natural language processing, and learning analytics work synergistically to create a learning experience that is not only personalized but also highly responsive to each student's unique needs. To comprehensively dissect this phenomenon, this article applies the Systematic Literature Review (SLR) approach by critically examining selected research indexed in the Scopus database. The goal is not just to map trends, but to identify existing research gaps and analyze the real impact of AI on learning outcomes. The findings of this review underscore the revolutionary potential of AI in democratizing access to education; This technology is able to increase students' cognitive engagement and motivation through materials tailored to their respective learning speeds and styles. However, this narrative of technological advancement does not come without complexity. Behind this optimism, the adoption of AI in the education sector is facing a significant wall of structural challenges. Crucial issues such as inequality of digital access that can widen the social gap, ethical dilemmas related to student data privacy, and the level of pedagogical readiness of educators in integrating these new tools, are the main highlights of this study. Therefore, this article exists as an effort to provide in-depth strategic insights for educators and policymakers, encouraging the design of AI-based education systems that are not only technically sophisticated, but also inclusive, ethical, and sustainable for the future.]]>
