Название: AI and IoT in the EduTech Era: The Future of Education
Автор: Yahya Fikri, Adnène Arbi
Издательство: CRC Press
Серия: Emerging Trends in Science, Innovation and Business
Год: 2027
Страниц: 193
Язык: английский
Формат: pdf (true)
Размер: 17.0 MB

Artificial Intelligence (AI) and the Internet of Things (IoT) are redefining the educational landscape across schools, universities, and training institutions worldwide. This book explores how these technologies are reshaping teaching, learning, assessment, governance, and institutional strategy in the evolving EduTech era.

Bringing together international scholars and practitioners, this volume examines the integration of AI and IoT from multiple perspectives: pedagogical, institutional, strategic, ethical, and environmental. It addresses resistance to technological change, the development of smart classrooms, prompt literacy for educators, AI-ready school models, and policy-to-practice transitions in higher education. The book also explores teacher identity transformation through AI co-teaching tools, equity challenges in global EdTech ecosystems, and empirical insights such as evidence from British universities. Furthermore, it analyzes smart diagnostics, personalized learning systems, sustainability concerns related to AI and IoT infrastructures, and the broader connection between digital transformation and education. Through conceptual frameworks, case studies, and policy analysis, the book provides a comprehensive roadmap for responsible and effective AI-driven educational innovation.

Machine Learning (ML) is at the centre of most development of an AI-based educational system, especially in the concept of learning analytics. ML algorithms are used to understand how learning trends, performance and behaviour relate to educational outcomes by analysing vast amounts of educational data such as educational assessments and recordings of learning activities and interactions as well as attendance records.

Traditional ML models such as regression and decision trees are common methods used in order to determine success in school and predict which students are at risk. More recently hot is Deep Learning (DL) techniques because of their capacity to record complex, no linear data relationships in high-dimensional data. Neural networks are now starting to be applied in the modelling of learning trajectories, content delivery or for systems implementing adaptive learning. While the ML and DL can increase the predictive accuracy and the scalability of the process, it is only through data quality, interpretability and by making valuable sense of it in pedagogical decision-making that the method becomes effective for education.

This book is intended for researchers, policymakers, school leaders, higher education administrators, EdTech professionals, and graduate students in education, digital transformation, and innovation management. It will also benefit practitioners seeking strategic guidance for implementing AI and IoT solutions in educational environments.