Artificial Intelligence in Education

This comprehensive volume explores the possibilities, challenges and ethical considerations of Artificial Intelligence (AI) in education through a machine-generated literature review that examines emerging research trends and findings. Each chapter presents summaries of pre-defined topics and includes a human-written introduction by the book editor. It covers critical areas such as educational data mining, learning analytics, personalised learning, adaptive assessment, intelligent tutoring systems, as well as the ethical challenges of AI in education. This volume provides valuable insights for educators, researchers, policymakers and students seeking to understand the transformative potential of AI in education. It serves as a reference point for navigating the evolving landscape of AI-assisted learning and offers a glimpse into the future of education in an AI-driven world.

The auto-summaries were generated by a recursive clustering algorithm using the Dimensions Auto-summariser from Digital Science. The editor of this book selected the SN content to be auto-summarised and decided the order of appearance. Please note that these are extractive auto-summaries, consisting of original sentences, but are not representative of the original paper, as we do not show the full length of the publication. Please note that only published SN content is represented here and that machine-generated books are still at an experimental stage.

A Machine-Generated Literature Overview of artificial intelligence in education Explores the ethical implications of artificial intelligence in education Discusses the future of education in an AI-driven world

Autorentext

Myint Swe Khine has more than 30 years of experience in teacher education. He holds master's degrees from the University of Southern California, USA, the University of Surrey, UK, and the University of Leicester, UK, and a doctorate from Curtin University, Australia. He has worked at the National Institute of Education, Nanyang Technological University, Singapore, and was a professor at the Emirates College for Advanced Education in the United Arab Emirates. He serves on the editorial boards of several international academic journals. Throughout his career, he has published over 40 edited books. His most recent volumes include Rhizomatic Metaphor: Legacy of Deleuze and Guattari in Education and Learning (Springer, 2023) and Machine Learning in Educational Sciences: Approaches, Applications and Advances (Springer, 2024). He currently teaches in the School of Education, Curtin University, Australia.

Inhalt

1. Educational Data Mining and Learning Analytics.- 2. Personalized learning with AI, Eye-tracking studies and Precision Education.- 3. Using AI for Adaptive Learning and Adaptive Assessment.- 4. AI in Teaching and Learning and Intelligent Tutoring Systems.- 5. Machine Learning in Education.- 6. Ethics, and the Future of Education in an AI-driven World.