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

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

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