Generative AI in Education: Pedagogy and Research

A Practical Guide for the AI-Enhanced Educational Landscape
Erscheinungsjahr 2026
ISBN: 978-981-9224-59-3
Verlag: Springer

Buch, Englisch, 368 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Lecture Notes in Educational Technology

This monograph examines the paradigm shift triggered by generative Artificial Intelligence (AI), particularly Large Language Models (LLMs) within educational contexts. It systematically investigates how these technologies redefine core pedagogical processes, ranging from instructional design and personalized tutoring to assessment and scholarly inquiry. A primary contribution of this work is the synthesis of technical, pedagogical, and critical discourses into a cohesive framework. By mapping the capabilities and constraints of generative AI onto established learning paradigms, including Constructivism, Cognitive Load Theory, and Connectivism, the text provides a robust theoretical foundation for technology integration. Furthermore, it operationalizes “Critical AI Literacy” as a fundamental competency, offering empirical methodologies for prompt engineering, assessment redesign, and the rigorous evaluation of machine-generated outputs. Ultimately, the book outlines a structural roadmap for responsible AI adoption, balancing applied pedagogical innovation with an in-depth examination of the underlying ethical, equity, and professional challenges.

Zielgruppe

Research

Autoren/Hrsg.

Liu, Zhi

Xu, Zhenguo

Du, Wenxiu

Fachgebiete

• Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik E-Learning, Bildungstechnologie
• Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik Methoden des Lehrens und Lernens

Inhaltsverzeichnis

Part I: Foundations and the New Educational Context.- Chapter 1: The Advent of Generative AI in Education.- Chapter 2: Understanding LLMs for Educational Stakeholders.- Chapter 3: Theoretical Frameworks for LLM Integration.- Part II: LLMs in Educational Practice and Design.- Chapter 4: Innovating Instructional Design with LLMs.- Chapter 5: Prompt Engineering as a Pedagogical Competency.- Chapter 6: Generative Content and Curriculum Development.- Chapter 7: Personalized Tutoring and Intelligent Intervention.- Chapter 8: Redefining Assessment in the Generative Age.- Part III: LLMs as Tools for Educational Research.- Chapter 9: Advancing Qualitative Research Methodologies.- Chapter 10: Innovations in Educational Data Mining and Learning Analytics.- Part IV: Critical Perspectives and Future Directions.- Chapter 11: Ethics, Equity, and Responsible Deployment.- Chapter 12: The Evolving Role of the Educator and Professional Development.- Chapter 13: Conclusion: A Roadmap for AIEd in the Generative Age.

Über Autor(innen)

Zhi Liu is a Professor, PhD Supervisor, and Deputy Head of the Department of Data Science at the Faculty of Artificial Intelligence in Education, Central China Normal University (CCNU). He is a researcher at the Laboratory for Artificial Intelligence and New Forms of Education and holds concurrent positions as a guest researcher at the Computer Science Institute of Humboldt University of Berlin and the German Research Center for Artificial Intelligence (DFKI). His research expertise encompasses text mining, educational data mining, and intelligent tutoring systems. As a key technical contribution, Dr. Liu developed an innovative intelligent tutoring system integrating knowledge and emotion awareness, which has empirically improved students' knowledge levels by 0.96 standard deviations. He has published over 60 papers in prominent SCI/SSCI journals, including Knowledge-Based Systems, Computers & Education, The Internet and Higher Education, and IEEE Transactions on Learning Technologies. Notably, 7 of these publications are ESI Highly Cited Papers (top 1%), and he has been recognized as a top 1% Highly Cited Scholar by the China National Knowledge Infrastructure (CNKI) for two consecutive years (2024–2025). In addition to his research, Dr. Liu actively serves the academic community. He is the Associate Editor of Educational Intelligence, a Guest Associate Editor for Frontiers in Artificial Intelligence, and an editorial board member for Acta Psychologica and Discover Education.

Zhenguo Xu is an Associate Professor at the School of Communication, Qufu Normal University. He is a “111” Leading Talent in Philosophy and Social Sciences of Shandong Province, leader of the “Educational Artificial Intelligence Innovation Team” under the Youth Innovation Team of Shandong Higher Education Institutions, and Deputy Director of the “Internet plus Education” Application Research Base of Shandong Province. His research focuses on educational artificial intelligence, online learning behavior, and digital learning resources. Dr. Xu has presided over more than 10 research projects funded by the National Natural Science Foundation of China, the Ministry of Education, the China Postdoctoral Science Foundation, and the Shandong Provincial Natural Science Foundation. He has published over 50 papers in SCI, SSCI, CSSCI, and EI-indexed journals, authored one monograph, and edited or co-edited several textbooks. He also serves as a reviewer for SCI, SSCI, and CSSCI journals, a project reviewer for the National Natural Science Foundation of China, and a session chair or program committee member for international conferences such as ICAIE, ICET, and CSTE.

Wenxiu Du is a doctoral student majoring in Educational Technology at the Faculty of Artificial Intelligence in Education, Central China Normal University (CCNU). Her research interests include educational artificial intelligence, educational data mining, and digital learning resources. As a major participant, she has contributed to research projects funded by the Ministry of Education, the Shandong Provincial Social Science Planning Project, the Shandong Provincial Natural Science Foundation, and other provincial-level research programs. Ms. Du has published multiple academic papers and has been honored with awards including Second Prize in the China Graduate Mathematical Modeling Contest, the National Scholarship for Graduate Students, and the Excellent Graduate Student title.