<![CDATA[The integration of the Internet of Things (IoT) into educational environments signifies a transformative shift towards smart classrooms, enabling real-time data-driven instruction, environmental optimization, and personalized learning experiences. This study explores the opportunities, challenges, and future directions of IoT deployment in academic settings through a mixed-methods approach that combines quantitative analysis, qualitative interviews, and IoT-edge data assessment. Survey responses from 150 educators and interviews with 20 key stakeholders revealed significant adoption rates and pedagogical benefits, including enhanced engagement and individualized feedback. However, critical challenges such as data privacy, cybersecurity risks, limited teacher training, and infrastructure disparities hinder widespread implementation. A machine learning framework utilizing Random Forest classification was applied to a custom IoT-edge dataset, uncovering correlations between environmental variables and student behavior. High temperatures negatively affected classroom occupancy, while increased light intensity correlated with heightened engagement. Model evaluation yielded strong performance metrics, including an accuracy of 95% and an AUC of 0.99, highlighting the predictive power of features like learning outcomes and engagement scores. The findings emphasize the dual importance of technical readiness and pedagogical adaptation, advocating for policy support, ethical data governance, and teacher capacity-building to fully realize IoT’s potential in shaping adaptive, equitable, and intelligent learning ecosystems.]]>
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