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(Samara University, Ethiopia)
Sheik Saidhbi is an Associate Professor in the Department of Computer Science at Samara University. With 25 years of teaching experience, she has made significant contributions to teaching, research, and academic mentoring. She obtained her Ph.D. from Bharathiar University and is currently pursuing Postdoctoral Research at SR University>. Her areas of interest include Machine Learning, Cloud Computing, Cyber Security, and Data Science. She has published over 40 research papers, authored two textbooks, and holds 10 patents and 4 copyrights.
The rapid advancement of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) is transforming higher education by enabling intelligent, personalized, and data-driven learning environments. Traditional educational systems often face challenges in monitoring student performance, identifying learning difficulties, promoting student wellbeing, and supporting academic innovation. To address these limitations, this study proposes an AI, ML, and IoT-enabled smart education framework that integrates real-time data collection, predictive analytics, and intelligent decision-support mechanisms to enhance student learning outcomes and institutional effectiveness. The framework utilizes IoT devices and smart sensors to gather data related to student attendance, engagement, learning behavior, and environmental conditions. Machine learning algorithms analyze the collected data to predict academic performance, identify at-risk students, and recommend personalized learning pathways. Artificial intelligence techniques further support adaptive learning, intelligent tutoring, and automated academic interventions. In addition, the proposed system incorporates student wellbeing indicators, including stress levels, participation patterns, and learning satisfaction, to foster a holistic educational experience. The integration of AI-driven analytics enables educational institutions to make informed decisions regarding curriculum design, resource allocation, and student support services. Experimental analysis demonstrates that the proposed framework improves learning efficiency, student engagement, academic achievement, and institutional innovation while facilitating data-driven educational management. The study highlights the potential of AI, ML, and IoT technologies to create sustainable, inclusive, and intelligent higher education ecosystems capable of meeting the evolving demands of digital learning environments and future workforce requirements.