Traditional education that generalizes all students has been increasingly abandoned. Awareness of the importance of personalization of learning for each student, coupled with technological developments, has fostered a smart learning model. Smart learning emphasizes the importance of technology design to make learning better through adaptation and personalization. This study aims to build a smart e-learning system framework to support personalized learning. PRISMA Statement, the systematic literature review used in this study, was carried out in four phases: identification, screening, eligibility, and interpreting the findings. From the first three phases, there were six articles that were eligible to be used for discussion. Based on the analysis of these articles, the framework was built. The macro-adaptive approach was applied at the beginning of the course. The student learning styles were determined by Felder-Silverman’s Index of Learning Style questionnaire. Personalization is done in three ways, namely, learning paths, content, and user interface. The micro-adaptive approach used during the course takes place in real time, based on adaptive engines built on artificial neural networks. Personalization can also be done manually by students and instructors. Consultation support between students and instructors is also provided.

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