Uses of Artificial Intelligence in STEM Education

Inbunden, Engelska, 2024

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

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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.

Produktinformation

Utgivningsdatum2024-10-24
Mått160 x 240 x 30 mm
Vikt1 140 g
FormatInbunden
SpråkEngelska
Antal sidor624
FörlagOUP OXFORD
ISBN9780198882077

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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.

Preface1: Xiaoming Zhai and Joseph Krajcik: Introduction: AI-based STEM Education: Challenges and OpportunitiesAI in STEM Assessment2: James W. Pellegrino: A New Era for STEM Assessment: Considerations of Assessment, Technology, and Artificial Intelligence3: Ross H. Nehm: AI in Biology Education Assessment: How Automation Can Drive Educational Transformation4: Marcia C. Linn and Libby Gerard: Assessing and Guiding Student Science Learning with Pedagogically Informed Natural Language Processing5: Changzhao Wang, Xiaoming Zhai, and Ji Shen: Applying Machine Learning to Assess Paper-Pencil Drawn Models of Optics6: Mei-Hung Chiu and Mao-Ren Zeng: Automated Scoring in Chinese Language for Science Assessments7: Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek: Exploring Attributes of Successful Machine Learning Assessments for Scoring of Undergraduate Constructed Response Assessment Items8: Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth Steimel: AI-based Diagnosis of Student Reasoning Patterns in NGSS AssessmentsAI Tools for Transforming STEM Learning9: Anna Herdliska and Xiaoming Zhai: Artificial Intelligence-Based Scientific Inquiry10: Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant: Supporting Simulation-mediated Scientific Inquiry through Automated Feedback11: Marcus Kubsch, Adrian Grimm, Knut Neumann, Hendrik Drachsler, Nikol Rummel: Using Evidence Centered Design to Develop an Automated System for Tracking Students>' Physics Learning in a Digital Learning Environment12: Janice D. Gobert, Haiying Li, Rachel Dickler, Christine Lott: Can AI-Based Scaffolding Support Students' Robust Learning of Authentic Science Practices?13: Ehsan Latif, Xiaoming Zhai, Holly Amerman, Xinyu He: AI-SCORER: An Artificial Intelligence-Augmented Scoring and Instruction System14: Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan Li: Smart Learning PartnerDLDLChinese Core Competency-oriented Adaptive Learning SystemAI-based STEM Instruction and Teacher Professional Development15: Lehong Shi, Ikseon Choi: A Systematic Review on Artificial Intelligence in Supporting Teaching Practice: Application Types, Pedagogical Roles, and Technological Characteristics16: Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik: A Design Framework for Integrating Artificial Intelligence to Support Teachers' Timely Use of Knowledge-in-Use Assessments17: 1. Abhijit Suresh, William R. Penuel, Jennifer K. Jacobs, Ali Raza, James H. Martin, Tamara Sumner: Using AI Tools to Provide Teachers with Fully Automated, Personalized Feedback on Their Classroom Discourse Patterns18: Lydia Bradford: Use of Machine Learning to Score Teacher Observations19: David Buschhüter, Marisa Pfläging, Andreas Borowski: Widening the Focus of Science Assessment via Structural Topic Modeling: An Example of Nature of Science Assessment20: Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton: 1. Classification of Instructional Activities in Classroom Videos Using Neural NetworksEthics, Fairness, and Inclusiveness of AI-based STEM Education21: Sahrish Panjwani-Charania, Xiaoming Zhai: AI for Students with Learning Disabilities: A Systematic Review22: Selin Akgun, Joseph Krajcik: 1. Artificial Intelligence (AI) as the Growing Actor in Education: Raising Critical Consciousness Towards Power and Ethics of AI in K-12 STEM Classrooms23: Wanli Xing, Chenglu Li: Fair Artificial Intelligence to Support STEM Education: A Hitchhiker's Guide24: Marvin Roski, Anett Hoppe, Andreas Nehring: Supporting Inclusive Science Learning through Machine Learning: The AIISE Framework25: Xiaoming Zhai & Joseph Krajcik: Pseudo Artificial Intelligence BiasConclusion26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM Education: A New Paradigm

Accelerating AI capabilities is introducing both the opportunity and the requirement to reimagine education. Artfully blending discussions of technology and education, Uses of Artificial Intelligence in STEM Education is a seminal collection exploring the profound impact that AI will have on learning, instruction, and assessment. With themes of ethics, fairness, and inclusiveness woven throughout the book, this is a must-read for anyone wanting to understand how AI will fundamentally reshape the educational landscape