Xiaoming Zhai – författare

In the age of rapid technological advancements, the integration of Artificial Intelligence (AI), machine learning (ML), and large language models (LLMs) in Science, Technology, Engineering, and Mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. Uses of AI in STEM Education, comprising 25 chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technological tools. As the chapters unfold, readers learn about innovative AI applications, from automated scoring systems in biology, chemistry, physics, mathematics, and engineering to intelligent tutors and adaptive learning. The book also touches upon the nuances of AI in supporting diverse learners, including students with learning disabilities, and the ethical considerations surrounding AI's growing influence in educational settings. It showcases the transformative potential of AI in reshaping STEM education, emphasizing the need for adaptive pedagogical strategies that cater to diverse learning needs in an AI-centric world. The chapters further delve into the practical applications of AI, from scoring teacher observations and analyzing classroom videos using neural networks to the broader implications of AI for STEM assessment practices. Concluding with reflections on the new paradigm of AI-based STEM education, this book serves as a comprehensive guide for educators, researchers, and policymakers, offering insights into the future of STEM education in an AI-driven world.

In the age of rapid technological advancements, the integration of Artificial Intelligence (AI), machine learning (ML), and large language models (LLMs) in Science, Technology, Engineering, and Mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. Uses of AI in STEM Education, comprising 25 chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technological tools. As the chapters unfold, readers learn about innovative AI applications, from automated scoring systems in biology, chemistry, physics, mathematics, and engineering to intelligent tutors and adaptive learning. The book also touches upon the nuances of AI in supporting diverse learners, including students with learning disabilities, and the ethical considerations surrounding AI's growing influence in educational settings. It showcases the transformative potential of AI in reshaping STEM education, emphasizing the need for adaptive pedagogical strategies that cater to diverse learning needs in an AI-centric world. The chapters further delve into the practical applications of AI, from scoring teacher observations and analyzing classroom videos using neural networks to the broader implications of AI for STEM assessment practices. Concluding with reflections on the new paradigm of AI-based STEM education, this book serves as a comprehensive guide for educators, researchers, and policymakers, offering insights into the future of STEM education in an AI-driven world.

This open access volume explores the transformative use of Artificial Intelligence (AI) as innovative methodologies in STEM education research. Featuring contributions from leading experts, it presents a rich collection of chapters that examine how AI tools—such as adaptive learning systems, automatic scoring, explainable AI, and intelligent tutoring—can be applied to enhance educational outcomes. Designed for researchers, educators, and graduate students, the book provides in-depth insights into AI as a tool for cutting-edge research methods and its potential to revolutionize research practices. Through practical examples, case studies, and methodological details, it serves as an essential resource for those looking to push beyond conventional methods and embrace the possibilities offered by AI in educational research.

This edited volume offers a timely, domain-specific examination of how artificial intelligence (AI) is reshaping the foundations of science teaching and learning. As AI transforms scientific work, societal expectations, and the skills future citizens need, science education stands at a pivotal crossroads. This book highlights the major shifts AI is driving—redefining educational goals, reshaping classroom procedures, expanding learning materials, enabling dynamic assessment, and altering the competencies students must develop to thrive in an AI-driven world of scientific inquiry and decision-making.Through critical analysis and vivid examples from core scientific practices, the authors reveal AI’s dual capacity to enrich and complicate science education. AI offers unprecedented personalization, efficiency, and access to authentic scientific practices, yet also raises concerns around fairness, transparency, privacy, accountability, and the preservation of human judgment. To navigate these tensions, this book introduces the Responsible and Ethical Principles (REP) framework, an action-oriented lens for guiding design, use, and governance that ensures AI advances equity, scientific integrity, and democratic participation. Scholars demonstrate how REP principles inform ethical goals, inclusive materials, trustworthy assessments, and transformative learning outcomes, sometimes constraining as well as enabling AI use as a partner rather than a replacement.Ultimately, Advancing AI in Science Education argues that even as AI reshapes schooling, science education must remain fundamentally human. Empathy, creativity, ethical judgment, and shared meaning-making within scientific communities anchor responsible AI-supported learning and guide an equitable, human-centered future for science education.