Abstract
The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) is reflected in the Internet of Educational Things (IoEd), in which connected devices and digital platforms are used to support learning environments. Research on Machine Learning (ML) in IoEd is fragmented, and comprehensive evaluation is limited. A systematic and bibliometric review of ML-based IoEd studies from 2015 to 2025 is presented, and a structured screening procedure is applied for study selection. A taxonomy that includes supervised, unsupervised, deep, and reinforcement learning is introduced, and applications in adaptive learning, personalized feedback, engagement monitoring, and resource management are examined. Key challenges are identified, i.e., data scarcity, class imbalance, device heterogeneity, limited interpretability, privacy concerns, and constraints in edge and fog com- puting environments. Future research directions are identified through the integration of generative AI and Large Language Models (LLMs), Explainable Artificial Intelligence (XAI), Federated Learn- ing (FL), Multimodal Learning Analytics (MMLA), digital twins, and energy efficient ML methods. The findings indicate that the integration of ML and IoEd supports the development of intelligent educational systems and requires interdisciplinary collaboration to ensure transparent, ethical, and scalable solutions.