Uses of Artificial Intelligence in STEM Education

by Zhai, Xiaoming; Krajcik, Joseph

• ISBN13: 9780198882077
• ISBN10: 0198882076
• Format: Hardcover
• Copyright: 2025-01-01
• Publisher: Oxford University Press

Summary

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.

Author Biography

Xiaoming Zhai, Associate Professor of Science Education and AI Director of AI4STEM Education Center, University of Georgia,Joseph Krajcik, University Distinguish Professor, Michigan State University

Xiaoming Zhai is an Associate Professor in Science Education & Artificial Intelligence, serving as Director of the AI4STEM Education Center at the University of Georgia. He is interested in applying cutting-edge technologies such as AI to advance science teaching and learning, particularly assessment practices. He is lead investigator on federal-funded projects and his research has been published in top-tier journals. He has collaborated widely with researchers from the USA, Canada, Germany, Norway, China, Ghana, and India, and serves as a global leader in his area of research. Dr. Zhai chaired the NSF-funded 2022 International Conference for AI-based Assessment in STEM and serves as Founding Chair of the National Association of Research in Science Teaching's RAISE (Research in AI-involved Science Education) group.

Joseph Krajcik currently serves as Director of the CREATE for STEM Institute at Michigan State University. CREATE for STEM (Collaborative Research for Education, Assessment and Teaching Environments for Science, Technology, Engineering, and Mathematics) is a joint institute between the Colleges of Natural Science and Education that seeks to improve the teaching and learning of science and mathematics from kindergarten to college through innovation and research. During his career, Professor Krajcik has focused on working with science teachers to reform science teaching practices to promote students' engagement in and learning of science through the design, development, and testing of project-based science learning environments.

Table of Contents

Preface1. Introduction: AI-based STEM Education: Challenges and Opportunities, Xiaoming Zhai and Joseph KrajcikAI in STEM Assessment2. A New Era for STEM Assessment: Considerations of Assessment, Technology, and Artificial Intelligence, James W. Pellegrino3. AI in Biology Education Assessment: How Automation Can Drive Educational Transformation, Ross H. Nehm4. Assessing and Guiding Student Science Learning with Pedagogically Informed Natural Language Processing, Marcia C. Linn and Libby Gerard5. Applying Machine Learning to Assess Paper-Pencil Drawn Models of Optics, Changzhao Wang, Xiaoming Zhai, and Ji Shen6. Automated Scoring in Chinese Language for Science Assessments, Mei-Hung Chiu and Mao-Ren Zeng7. Exploring Attributes of Successful Machine Learning Assessments for Scoring of Undergraduate Constructed Response Assessment Items, Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek8. AI-based Diagnosis of Student Reasoning Patterns in NGSS Assessments, Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth SteimelAI Tools for Transforming STEM Learning9. Artificial Intelligence-Based Scientific Inquiry, Anna Herdliska and Xiaoming Zhai10. Supporting Simulation-mediated Scientific Inquiry through Automated Feedback, Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant11. Using Evidence Centered Design to Develop an Automated System for Tracking Students’ Physics Learning in a Digital Learning Environment, Marcus Kubsch, Adrian Grimm, Knut Neumann, Hendrik Drachsler, Nikol Rummel12. Can AI-Based Scaffolding Support Students' Robust Learning of Authentic Science Practices?, Janice D. Gobert, Haiying Li, Rachel Dickler, Christine Lott13. AI-SCORER: An Artificial Intelligence-Augmented Scoring and Instruction System, Ehsan Latif, Xiaoming Zhai, Holly Amerman, Xinyu He14. Smart Learning Partner——Chinese Core Competency-oriented Adaptive Learning System, Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan LiAI-based STEM Instruction and Teacher Professional Development15. A Systematic Review on Artificial Intelligence in Supporting Teaching Practice: Application Types, Pedagogical Roles, and Technological Characteristics, Lehong Shi, Ikseon Choi16. A Design Framework for Integrating Artificial Intelligence to Support Teachers' Timely Use of Knowledge-in-Use Assessments, Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik17. Using AI Tools to Provide Teachers with Fully Automated, Personalized Feedback on Their Classroom Discourse Patterns, 1. Abhijit Suresh, William R. Penuel, Jennifer K. Jacobs, Ali Raza, James H. Martin, Tamara Sumner18. Use of Machine Learning to Score Teacher Observations, Lydia Bradford19. Widening the Focus of Science Assessment via Structural Topic Modeling: An Example of Nature of Science Assessment, David Buschhüter, Marisa Pfläging, Andreas Borowski20. 1. Classification of Instructional Activities in Classroom Videos Using Neural Networks, Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. ActonEthics, Fairness, and Inclusiveness of AI-based STEM Education21. AI for Students with Learning Disabilities: A Systematic Review, Sahrish Panjwani-Charania, Xiaoming Zhai22. 1. Artificial Intelligence (AI) as the Growing Actor in Education: Raising Critical Consciousness Towards Power and Ethics of AI in K-12 STEM Classrooms, Selin Akgun, Joseph Krajcik23. Fair Artificial Intelligence to Support STEM Education: A Hitchhiker's Guide, Wanli Xing, Chenglu Li24. Supporting Inclusive Science Learning through Machine Learning: The AIISE Framework, Marvin Roski, Anett Hoppe, Andreas Nehring25. Pseudo Artificial Intelligence Bias, Xiaoming Zhai & Joseph KrajcikConclusion26. Conclusions and Foresight on AI-based STEM Education: A New Paradigm, Xiaoming Zhai