Designing Smart Learning Environments for Personalized Education: Investigating the Influence of Adaptive Technologies on Student Learning Experiences

The rapid integration of artificial intelligence, learning analytics, intelligent tutoring, adaptive assessment, and connected learning technologies is reshaping higher education learning environments. However, the literature often treats smart learning environments, adaptive technologies, personalized learning, and student experience as separate research streams. This paper integrates these perspectives by identifying five recurring mechanisms: smart-environment infrastructure, learner modelling, adaptive content and assessment, feedback and visualization, and learner agency and self-regulation. The synthesis indicates that adaptive systems are most consistently associated with improved learning fit, engagement, self-regulation, and perceived usefulness when adaptation is timely, interpretable, and connected to meaningful learning tasks. However, technology alone does not guarantee better learning experiences; outcomes depend on how learner data inform pedagogical decisions, the degree of learner control, instructor involvement, and attention to privacy, transparency, and equity. Based on these findings, the paper proposes the Smart Personalized Learning Experience Framework (SPLEF), which models a pathway from smart-environment capabilities through adaptive technologies and personalization quality to learner processes and learning experience outcomes. The framework provides a design-oriented basis for advancing learner-centred, data-informed, adaptive, and ethically governed personalization in higher education.