Teaching and Learning in the Era of Generative AI
Description
This edited volume showcases how generative AI-especially large language and multimodal models-is revolutionizing education. Discover how these technologies personalize learning, spark creativity, and transform assessment, while addressing critical issues of trust, ethics, and fairness. Featuring leading voices from education and AI, the book explores real-world applications, innovative classroom practices, and the evolving role of teachers in AI-driven environments. With peer-reviewed chapters presented at an AIED workshop, this volume is a must-read for educators, researchers, and practitioners seeking to harness AI for inclusive, future-ready learning.
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Persons
Dr. Xiaoming Zhai Associate Professor at the University of Georgia, USA and Director of the National GENIUS and AI4STEM Centers. His research explores AI-based assessment and AI in science teaching to enhance STEM learning. He has published widely and edited books on AI in STEM education. Dr. Zhai co-founded and chairs the RAISE group within NARST.
Ehsan Latif Postdoctoral Research Associate at the AI4STEM and GENIUS Centers, University of Georgia, USA. His work focuses on AI in education, leveraging large language models for assessment and content generation. He has led NSF-funded projects, published in top journals, and earned awards for research excellence, including the Pioneers of Natural Sciences Laboratory Robotics Award.
Prof. Gautam Biswas Researcher applying AI to create open-ended learning environments for STEM and CS education. He develops multimodal analytics to study self-regulated and collaborative learning, linking behaviors to progress. His work spans K-12 to professional training and is funded by NASA, NSF, the Army, and the U.S. Department of Education.
Content
1. Intro Chapter.- Part I: Educators and Learners in the Age of AI.- 1. Educator Perspectives on a Generative AI Tool to Support Information Problem Solving for Epistemic Decision Making.- 2. Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval Augmented Generation (RAG).- ChatGPT user experience: Implications for education.- Part II: Teacher Knowledge and Professional Development.- 5. Using Large Language Models to Assess Teachers' Pedagogical Content Knowledge.- 6. Assessing Chinese Pre-service Teachers' Intelligent-TPACK.- Part III: Assessment and Learning with LLMs.- 7. LLM Prompt Evaluation for Educational Applications.- 8. G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in German.- 9. Criteria for Demonstration Examples for LLM Assessment of Science Essays.- Part IV: Ethics, Fairness, and Future Directions.- 10. AI Gender Bias, Disparities, and Fairness: Does Training Data Matter?.- 11. Education in the Era of Artificial General Intelligence: A Review of the Literature.- 12. Concluding chapter.