| dc.description.abstract | The integration of machine learning (ML) into adaptive learning platforms has signifi-
cantly advanced personalized education in higher education, offering effective solutions
to challenges such as student engagement, retention, and dropout prevention. This
systematic review, conducted following PRISMA guidelines, synthesizes empirical re search published between 2020 and 2025 to explore how ML-powered adaptive systems
personalize learning through real-time behavioral analytics, learning style detection,
and dynamic content adaptation. The review highlights key technologies, including
multilayer perceptrons, support vector machines, and reinforcement learning, which
enable individualized learning pathways tailored to cognitive styles and preferences.
Evidence from diverse global contexts, including Sri Lanka, demonstrates improved
academic performance, enhanced learner engagement, and reduced dropout rates.
However, persistent challenges include scalability, data privacy, algorithmic bias, and
the need for human oversight to ensure ethical implementation. The paper concludes by
recommending best practices for integrating adaptive systems in higher education and
outlines future research directions focusing on long-term impacts, cross-disciplinary
applications, and socio-emotional learning integration. This review underscores the
transformative potential of ML-enhanced adaptive learning platforms to foster equitable,
personalized, and learner-centered education. | en_US |
