Uses of Artificial Intelligence in STEM Education

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.

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