Artificial Intelligence Adoption in Learning Systems: Systematic Literature Review - Hendi Sama; Aisyah Nurkayla; Stefanus Eko Prasetyo (Universitas Trilogi, 2026)
The integration of Artificial Intelligence (AI) in education has accelerated rapidly, reshaping teaching and learning processes through intelligent, adaptive, and data-driven systems. Despite its widespread adoption, a consolidated understanding of implementation trends, benefits, and challenges across educational contexts remains limited. This study aims to examine the current state of AI adoption in education by identifying key applications, success factors, and implementation barriers. A systematic literature review (SLR) was conducted by analyzing 32 peer-reviewed journal articles published within the last five years and indexed in reputable academic databases. The reviewed studies focus on adaptive learning systems, intelligent tutoring systems, and learning analytics. The findings demonstrate that AI contributes significantly to personalized learning, real-time performance assessment, and improved learner engagement. However, effective implementation is strongly influenced by institutional readiness, educator digital literacy, data quality, and ethical governance. Major challenges identified include data privacy concerns, lack of standardization, and unequal access to technological infrastructure. This study concludes that AI has substantial potential to support more adaptive and inclusive educational systems, but sustainable integration requires coordinated efforts among educators, policymakers, and technology developers.