Personalized Learning with Artificial Intelligence: A Systematic Literature Review on Implementation and Outcomes in Educational Settings - Rifai, Alfan; AN, Andri Nirwana; Wachidi, Wachidi; Budihargo, Mushab Umar (Universitas Muhammadiyah Surakarta, 2025)
Purpose: This study examines the application of artificial intelligence (AI) in education, with an emphasis on personalized learning across K–12, higher education, and online learning, and evaluates both its benefits and implementation challenges. Methodology: The research uses a qualitative literature-based synthesis of empirical and conceptual studies on AI-enabled personalized learning. Evidence is compared across education levels and learning domains to identify (1) observed learning impacts (e.g., engagement and achievement) and (2) recurring adoption barriers (e.g., infrastructure readiness, equity, and governance). Results: The findings indicate that AI-supported personalized learning can improve learner engagement and motivation by delivering adaptive content and feedback. Reported outcomes include performance gains in adaptive learning implementations, such as an increase in average scores from 68.4 to 82.7. However, implementation obstacles remain substantial, including limited infrastructure, unequal access, insufficient educator training, data privacy risks, and institutional resistance to change. Several studies also highlight unintended effects such as learner over-reliance on technology and reduced teacher–student social interaction. Applications/Originality/Value: This study consolidates cross-level evidence on where AI personalization is most effective and where it is most fragile. It provides practical implications for policymakers and institutions: invest in infrastructure, deliver sustained teacher training, and enforce strict data protection governance to enable responsible adoption. The study argues for a balanced integration model that leverages AI’s adaptivity while preserving human-centered educational values.