Integration of Artificial Intelligence (AI) in Adaptive Learning: A Personalized Analysis of Student Learning Experience in the Digital Era - Sofwan Manaf; Ahsanul Mufid (CV. Kayaswara, 2026)
The integration of Artificial Intelligence (AI) in adaptive learning has become a strategic approach to personalizing student learning experiences in the digital era. This study aims to analyze the implementation of AI-based adaptive learning systems in personalizing learning experiences for university students. Using a qualitative approach and a systematic literature review (SLR), this research analyzed 35 peer-reviewed articles published between 2015 and 2025, sourced from Scopus, Web of Science, and ERIC databases, following the PRISMA guidelines. Data analysis was conducted through thematic coding and narrative synthesis. The findings reveal three key themes: (1) AI-driven adaptive learning systems significantly enhance personalized content delivery through machine learning algorithms, intelligent tutoring systems, and learning analytics; (2) the integration of AI in learning personalization positively impacts student engagement, academic performance, and self-regulated learning; and (3) critical challenges persist including digital infrastructure gaps, data privacy concerns, algorithmic bias, and the need for educator readiness. This study contributes to the existing literature by providing a comprehensive analysis of how AI technologies can be strategically integrated into higher education to create inclusive, responsive, and learner-centered educational ecosystems in the digital era.