The transformation of educational environments through the integration of Internet of Things (IoT) and Artificial Intelligence (AI) has enabled the development of personalized and adaptive learning pathways that optimize student engagement, performance, and holistic development. IoT-enabled devices facilitate continuous monitoring of learner behaviors, cognitive states, and interaction patterns, generating high-resolution datasets that inform adaptive instructional strategies. AI-driven analytics leverage these data to construct dynamic learner profiles, predict academic challenges, and provide context-aware recommendations for content delivery, pacing, and remediation. The convergence of IoT and AI enhances real-time responsiveness, supports self-regulated learning, and promotes skill acquisition beyond conventional academic metrics. Implementation of these adaptive systems in higher education and K-12 settings presents challenges, including infrastructure limitations, algorithmic transparency, data privacy, and educator readiness. Case studies demonstrate that effective integration of IoT and AI can significantly improve learning outcomes, engagement, and motivation while enabling evidence-based instructional decision-making. Ethical considerations, inclusive design, and scalable frameworks are critical for fostering trust and ensuring equitable access to personalized learning experiences. The chapter provides a comprehensive analysis of current methodologies, deployment strategies, and research gaps, offering insights for future development of intelligent, adaptive educational ecosystems.
The evolution of educational technology has redefined the methods through which knowledge is imparted and acquired, emphasizing personalized learning experiences tailored to individual student requirements [1–3]. Traditional instructional paradigms, characterized by uniform content delivery and fixed pacing, often fail to address the cognitive, motivational, and engagement differences among learners [4,5]. Personalized and adaptive learning pathways offer a solution by dynamically adjusting instructional content, difficulty, and pacing according to the learner’s unique profile [6–8]. The integration of Internet of Things (IoT) devices in classrooms, such as smart wearables, sensor-enabled tablets, and connected educational platforms, enables continuous monitoring of behavioral and cognitive patterns [9–11]. These devices capture granular data on attention, participation, and interaction, providing educators and intelligent systems with the foundation to create data-driven interventions that enhance comprehension, retention, and skill development across diverse learning populations [12,13].
Artificial Intelligence (AI) plays a pivotal role in analyzing the extensive data collected through IoT devices, transforming raw information into actionable insights for optimizing instructional strategies [14–16]. Machine learning algorithms, deep learning models, and predictive analytics identify trends in student performance, anticipate learning difficulties, and provide tailored recommendations for content delivery [17–19]. Adaptive learning systems leverage these insights to generate dynamic learning pathways that respond to evolving student needs in real-time [20,21]. Natural Language Processing (NLP) further enriches the system by offering contextual feedback, guiding learners through complex concepts, and detecting misconceptions before they manifest in assessments [22,23]. These AI-enabled interventions allow educators to focus on facilitating higher-order thinking, fostering creativity, and enhancing collaborative learning while ensuring that each learner receives personalized guidance and support throughout their academic journey [24,25].
