A Learner-First Pedagogical Framework for Smart Learning Environments: Aligning Learner Needs Analysis with Intelligent Educational Technologies

Abstract

The rapid evolution of digital infrastructure has transformed conventional classrooms into Smart Learning Environments (SLEs). However, many implementations emphasize technological sophistication over pedagogical effectiveness and learner-centric design. This research proposes a comprehensive pedagogical framework grounded in Learner Needs Analysis (LNA) as the foundational element for sustainable smart education. The research examines the role of systematic learner profiling, cognitive load, digital literacy, and learning styles in the effective integration of Artificial Intelligence and Internet of Things in education. Employing a mixed-methods strategy, the framework combines Constructivist and Connectivist theories of learning in a cloud-based platform to match learner needs with smart system capabilities. Diagnostic learner profiles were created and flexible learning support was implemented and assessed. The results show that differentiated learning supported by learning analytics has a significant positive effect on learner engagement, self-directed learning, and cognitive overload. The research concludes that intelligence in smart learning systems is a product of adaptive and responsive learning pedagogy, not just technology.