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This edited volume explores the responsible and ethical integration of artificial intelligence (AI) science education, examining its impact on teaching, learning, assessment, and broader educational goals. It begins by mapping the current landscape of AI in science classrooms and the ethical questions surrounding its use. The chapters highlight how AI can advance science literacy, inspire creative student engagement, and transform instructional practices, including teacher professional development and the design of learning materials. The book also investigates AI-driven assessment strategies, addressing challenges such as bias and fairness. Ultimately, it considers how AI is reshaping science education, preparing both students and educators to navigate an AI-infused world with responsibility, equity, and critical awareness.
Chapters 2 and 11 are licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/)
List of contents
The landscape of ai in science education what is changing and how to respond.- AI in contemporary science practice implications for ai integrated science education.- Responsible and ethical principles for the practice of ai supported science education.- AI enhanced science literacy science literacy plus.- AI transformations of science education goals to prepare students as lifelong investigators.- Science teaching and learning with artificial intelligence.- Capitalizing on ai to create critical entry points to support science teacher learning and the work of science teaching.- Transforming science learning materials in the era of artificial intelligence.- Addressing challenges of science assessment with artificial intelligence.- AI based science assessment potential pitfalls and responsible and ethical practice.- Preparing students for living in an ai infused world revisiting the outcomes of science education.- Charting the future of ai supported science education a human centered vision.
About the author
Xiaoming Zhai, Associate Professor in Science Education and AI, (courtesy) Associate Professor in Computer Science and in Statistics, serves as Directors of the AI4STEM Education Center and the National GENIUS Center at University of Georgia, USA. His research pioneers the use of AI to transform STEM education. He has published widely, serves as guest editor of various journals, chaired the 2022 and 2025 AI in STEM education conferences, and co-founds and -leads NARST’s RAISE group. He edited Uses of Artificial Intelligence in STEM Education (Oxford, 2024) and Artificial Intelligence in STEM Education Research (Springer, 2026). Dr. Zhai is a leading voice in shaping the future of AI for STEM education.
Kent J. Crippen, Professor of STEM Education at University of Florida, USA. His research advances a resilient STEM workforce by designing environments that immerse students in authentic science and engineering practices. The Crippen Research Group investigates learning and participation across interdisciplinary contexts, including extended reality, AI and data science, engineering design, and molecular modeling. With strong funding and national honors, including as AAAS Fellow, Crippen’s scholarship emphasizes innovation, integrity, impact, and teamwork. He serves as Editor-in-Chief of the Journal of Science Education and Technology, founding editor of the Advances in Technology-Rich Science Education book series, and co-leads NARST’s RAISE RIG.
Summary
This edited volume explores the responsible and ethical integration of artificial intelligence (AI) science education, examining its impact on teaching, learning, assessment, and broader educational goals. It begins by mapping the current landscape of AI in science classrooms and the ethical questions surrounding its use. The chapters highlight how AI can advance science literacy, inspire creative student engagement, and transform instructional practices, including teacher professional development and the design of learning materials. The book also investigates AI-driven assessment strategies, addressing challenges such as bias and fairness. Ultimately, it considers how AI is reshaping science education, preparing both students and educators to navigate an AI-infused world with responsibility, equity, and critical awareness.
