Learning with Generative Artificial Intelligence

This book delves into the core of education's digital transformation, presenting a thorough and empirical examination of generative artificial intelligence (GenAI)'s impact beyond the theoretical and fragmented insights prevalent in current discourse. Drawing from peer-reviewed and extensive empirical studies, the contributors aim to unveil the multifaceted effects of GenAI (particularly ChatGPT) on learning. They navigate through topics of interaction, assessment, emotion, effect and efficiency, meta-cognition, and ethics, offering a comprehensive exploration of GenAI's educational implications. This book presents a closed loop of learning theory, multimodal data, and learning analytics technology. Furthermore, this book builds and proposes core conceptual models for future learning and identifies potential research directions. This book will serve as a foundational reference for educators seeking innovative learning and teaching methods and for researchers and technologists who seek to push the boundaries of educational technology and related areas.

Yizhou Fan is an Assistant Professor at the Graduate School of Education, Peking University and an Adjunct Research Fellow at the Centre for Learning Analytics, Monash University. He identifies himself as a learning analyst employing computational techniques to enhance the understanding of self-regulated learning and to develop next-generation learning environments for envisioning future education. In 2023, he received the Emerging Scholars Award and Early Career Research Grant from SoLAR (The Society for Learning Analytics Research). His recent research focuses on human-AI collaboration and the scaffolding of hybrid intelligence.

1 Brief History of AI in Education and Transforming Learning with Generative Artificial Intelligence 2 Empirical Research Design that Linking Theoretical Concepts and Empirical Data: learning with GenAI or human teacher 3 Enhancing Learning Through Interaction with GenAI: Opportunities, Challenges and Future Directions 4 Learners' Emotion and Motivation while Learning with GenAI 5 The Impacts of GenAI on Learning: What Works and What Falls Short? 6 How GenAI Affects Metacognition In Self-Regulated Learning: between Enhancement and Inhibition 7 Enhance Assessing Students' Learning with GenAI: Challenges, Opportunities and Future Directions 8 Ethical Issues and Value Tensions in the Context of GenAI-assisted Learning 9 Future Vision and Key Topics of Learning with GenAI: Conceptual Constructions Rooted in Empirical Studies