Artificial Intelligence and IoT in Online Education Systems

Design the future of digital education with this essential book that provides a comprehensive guide to leveraging AI and IoT to create dynamic, inclusive virtual learning environments and effectively implement advanced online proctoring solutions. The rapid development of online learning environments and virtual classrooms, coupled with the need for scalable, personalized education systems, has positioned AI as a key enabler of modern education. The advent of these technologies promises to reshape how we deliver, monitor, assess, and evaluate online learning. This book explores these critical intersections of technology and education, emphasizing the potential of AI and IoT not only to optimize outcomes but also to create more dynamic, responsive, and inclusive virtual learning environments. Focusing on problems that can be solved through computer vision, video and audio streaming, class imbalance data, audio-to-text processes, multi-modal and bi-modal aspects, hand-written strokes, text similarity, biomedical ethics, and advancements in machine and deep learning algorithms, this book comprehensively explores the effectiveness of these technologies in online proctoring. This essential guide will equip educators, technologists, administrators, and policymakers with the knowledge and perspective necessary to leverage these technologies effectively. Readers will find the book:

• Explores various AI tools and techniques adopted for online proctoring examination systems;
• Covers critical analytical aspects of AI-assisted systems;
• Describes a variety of experiments leading to uni- and multi-modal systems and IoT-based architecture using computer vision, machine learning, and deep learning algorithms;
• Discusses the quality assurance and psychological aspects to preserve ethics during examinations.

Audience Educational researchers and policymakers, as well as computer scientists working in AI, machine learning, data science, deep learning, computer vision, and statistics. Ramanujam E., PhD is an Assistant Professor in the Department of Computer Science and Engineering at the National Institute of Technology Silchar, Assam, India. He has contributed significantly to human activity recognition, especially online education and examinations, spam detection, and feature engineering across various domains. His areas of expertise include artificial intelligence, machine learning, deep learning, and ambient intelligence. Chandan Chakraborty, PhD is a Professor in the Department of Computer Science and Engineering at the National Institute of Technical Teachers’ Training and Research, Kolkata, West Bengal, India. He has more than 100 peer-reviewed publications in top journals and holds several patents. He specializes in artificial intelligence, machine learning, deep learning, and biomedical engineering.