• 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 Partner--Chinese 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 and Joseph Krajcik: Pseudo Artificial Intelligence Bias



• Conclusion



• 26: Xiaoming Zhai: Conclusions and Foresight on AI-based STEM Education: A New Paradigm