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.
As technology rapidly evolves, AI tools such as automated scoring and intelligent tutors are revolutionizing how we teach STEM subjects. The book discusses the benefits, challenges, and ethical implications. It's a comprehensive guide that showcases the future of education in an AI-driven world.
Recenzijos
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 * James Lester, Goodnight Distinguished University Professor in Artificial Intelligence and Machine Learning at North Carolina State University and Director of the National Science Foundation AI Institute for Engaged Learning * This volume speaks to all science education scholars, offering a broad-ranging description of the field's current state regarding the integration of AI. Zhai and Krajcik use their wide-ranging experience to couple a comprehensive exploration of the topic with a critical evaluation and reflection on the progress to date and the significant challenges ahead. In light of the rapidly evolving nature of this phenomenon, the insights provided here are exceptionally important. * Kent J. Crippen, Irving and Rose Fien Endowed Professor of STEM Education, University of Florida * Science educators have long pioneered the integration of emerging computing tools into the research and practices of STEM education... Zhai and Krajcik's foundational volume will accelerate critical thinking about and research-informed appropriations of frontier artificial intelligence advances into the richly interdisciplinary STEM education field. Our AI in Education scholarship benefits from their authors' contributions to the four themes of inquiry for the book's chaptersassessment, learning tools, teacher development, and ethics. * Roy Pea, David Jacks Professor of Education & Learning Sciences at Stanford University * Artificial intelligence (AI) dominates much of educational discourse these days. This comprehensive edited volume enriches the discussion. The book presents the benefits and challenges of incorporating AI into STEM education, particularly streamlining teaching practices and allowing inquiry-based and individualized learning approaches. Throughout, researchers stress the importance of interdisciplinary and action-based research that enhances-not entirely replaces-the human aspects of education with AI. This is also an open-access book, facilitating engagement with its content to further this emerging field collaboratively. * E. G. Harrington, Choice *
Preface
1: Xiaoming Zhai and Joseph Krajcik: Introduction: AI-based STEM Education:
Challenges and Opportunities
AI in STEM Assessment
2: James W. Pellegrino: A New Era for STEM Assessment: Considerations of
Assessment, Technology, and Artificial Intelligence
3: Ross H. Nehm: AI in Biology Education Assessment: How Automation Can Drive
Educational Transformation
4: Marcia C. Linn and Libby Gerard: Assessing and Guiding Student Science
Learning with Pedagogically Informed Natural Language Processing
5: Changzhao Wang, Xiaoming Zhai, and Ji Shen: Applying Machine Learning to
Assess Paper-Pencil Drawn Models of Optics
6: Mei-Hung Chiu and Mao-Ren Zeng: Automated Scoring in Chinese Language for
Science Assessments
7: Megan Shiroda, Jennifer Doherty, and Kevin C. Haudek: Exploring Attributes
of Successful Machine Learning Assessments for Scoring of Undergraduate
Constructed Response Assessment Items
8: Lei Liu, Dante Cisterna, Devon Kinsey, Yi Qi, Kenneth Steimel: AI-based
Diagnosis of Student Reasoning Patterns in NGSS Assessments
AI Tools for Transforming STEM Learning
9: Anna Herdliska and Xiaoming Zhai: Artificial Intelligence-Based Scientific
Inquiry
10: Hee-Sun Lee, Gey-Hong Gweon, and Amy Pallant: Supporting
Simulation-mediated Scientific Inquiry through Automated Feedback
11: 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 Environment
12: 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 System
14: Lei Wang, Cong Wang, Quan Wang, Jiutong Luo, Xijuan Li: Smart Learning
PartnerDLDLChinese Core Competency-oriented Adaptive Learning System
AI-based STEM Instruction and Teacher Professional Development
15: Lehong Shi, Ikseon Choi: A Systematic Review on Artificial Intelligence
in Supporting Teaching Practice: Application Types, Pedagogical Roles, and
Technological Characteristics
16: Peng He, Namsoo Shin, Xiaoming Zhai, Joseph Krajcik: A Design Framework
for Integrating Artificial Intelligence to Support Teachers' Timely Use of
Knowledge-in-Use Assessments
17:
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 Patterns
18: Lydia Bradford: Use of Machine Learning to Score Teacher Observations
19: David Buschhüter, Marisa Pfläging, Andreas Borowski: Widening the Focus
of Science Assessment via Structural Topic Modeling: An Example of Nature of
Science Assessment
20: Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott
T. Acton:
1. Classification of Instructional Activities in Classroom Videos
Using Neural Networks
Ethics, Fairness, and Inclusiveness of AI-based STEM Education
21: Sahrish Panjwani-Charania, Xiaoming Zhai: AI for Students with Learning
Disabilities: A Systematic Review
22: 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 Classrooms
23: Wanli Xing, Chenglu Li: Fair Artificial Intelligence to Support STEM
Education: A Hitchhiker's Guide
24: Marvin Roski, Anett Hoppe, Andreas Nehring: Supporting Inclusive Science
Learning through Machine Learning: The AIISE Framework
25: Xiaoming Zhai & Joseph Krajcik: Pseudo Artificial Intelligence Bias
Conclusion
26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM Education: A
New Paradigm
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.
