Intelligent Learning Environments Based on the Integrated Use of Artificial Intelligence, IoT, and Educational Data Analytics

Abstract

This paper proposes an approach to the development of an intelligent learning environment based on the integrated use of Artificial Intelligence (AI), the Internet of Things (IoT), and Learning Analytics. A multi-layer architecture is developed to support the collection of heterogeneous data from Learning Management Systems (LMSs), digital educational resources, assessment systems, and IoT devices, followed by their integration and analytical processing, the construction of a dynamic learner profile, and the prediction of learning outcomes. An AI-based learning adaptation model is proposed that takes into account the level of learning material mastery, learner engagement, behavioral and contextual characteristics, and the risk of declining academic performance. Based on the learner's current state and predicted outcomes, the system selects a personalized adaptive action, which may involve adjusting task difficulty, modifying the individual learning pathway, recommending supplementary learning resources, or notifying the instructor. A distinctive feature of the proposed approach is the implementation of a closed-loop framework that integrates data acquisition and integration, Learning Analytics, AI-based assessment and prediction, adaptive decision-making, implementation of adaptive interventions, and subsequent updating of the learner profile. The proposed approach provides a foundation for transitioning from passive monitoring of educational data to dynamic and personalized management of the learning process.