Artificial Intelligence in STEM Education

Dr. Fan Ouyang is a research professor in the College of Education at Zhejiang University. Dr. Ouyang holds a Ph.D. degree from the University of Minnesota. Her research interests are computer-supported collaborative learning, learning analytics and educational data mining, online and blended learning, and artificial intelligence in education. Dr. Ouyang has authored/coauthored more than 30 SSCI/SCI/EI papers and conference publications and worked as PI/co-PI on more than 10 research projects, supported by National Science Foundation of China (NSFC), Zhejiang Province Educational Reformation Research Project, Zhejiang Province Educational Science Planning and Research Project, Zhejiang University-UCL Strategic Partner Funds, etc.Dr. Pengcheng Jiao is a research professor in the Ocean College at the Zhejiang University, China. His multidisciplinary research integrates structures and materials, sensing, computing, networking, and robotics to create and enhance the smart ocean. His research interests include mechanical functional metamaterials, SHM and energy harvesting, marine soft robotics and AIEd. In recent years, he has authored/co-authored more than 100 peer-reviewed journal and conference publications and worked as PI/co-PI on more than 10 research projects. Dr. Bruce M. McLaren is an Associate Research Professor at Carnegie Mellon University, current Secretary and Treasurer and past President of the International Artificial Intelligence in Education Society (2017-2019). McLaren is passionate about how technology can support education and has dedicated his work and research to projects that explore how students can learn with educational games, intelligent tutoring systems, e-learning principles, and collaborative learning. He holds a Ph.D. and M.S. in Intelligent Systems from the University of Pittsburgh, an M.S. in Computer Science from the University of Pittsburgh, and a B.S. in Computer Science (cum laude) from Millersville University. Dr. Amir H. Alavi is an Assistant Professor in the Department of Civil and Environmental Engineering and Department of Bioengineering at the University of Pittsburgh. He holds a PhD degree in Civil Engineering from Michigan State University. His original and seminal contributions to developing and deploying advanced machine learning and bio-inspired computation techniques have established a road map for their broad applications in various engineering domains. He is among the Web of Science ESI's World Top 1% Scientific Minds in 2018, and the Stanford University list of Top 1% Scientists in the World in 2019 and 2020.

• Section I: AI-Enhanced Adaptive, Personalized Learning 1. Artificial intelligence in STEM education: current developments and future considerations 2. Towards a deeper understanding of K-12 students' CT and engineering design processes 3. Intelligent science stations bring AI tutoring into the physical world 4. Adaptive Support for Representational Competencies during Technology-Based Problem Solving in STEM 5. Teaching STEM subjects in non-STEM degrees: An adaptive learning model for teaching Statistics 6. Removing barriers in self-paced online learning through designing intelligent learning dashboards Section II: AI-Enhanced Adaptive Learning Resources 7. PASTEL: Evidence-based learning engineering methods to facilitate creation of adaptive online courseware 8. A Technology-Enhanced Approach for Locating Timely and Relevant News Articles for Context-Based Science Education 9. Adaptive learning profiles in the education domain Section III: AI-Supported Instructor Systems and Assessments for AI and STEM Education 10. Teacher orchestration systems supported by AI: Theoretical possibilities and practical considerations 11. The role of AI to support teacher learning and practice: A review and future directions 12. Learning outcome modeling in computer-based assessments for learning 13. Designing automated writing evaluation systems for ambitious instruction and classroom integration Section IV: Learning Analytics and Educational Data Mining in AI and STEM Education 14. Promoting STEM education through the use of learning analytics: A paradigm shift 15. Using learning analytics to understand students’ discourse and behaviors in STEM education 16. Understanding the role of AI and learning analytics techniques in addressing task difficulties in STEM education 17. Learning analytics in a Web3D-based inquiry learning environment 18. On machine learning methods for propensity score matching and weighting in educational data mining applications 19. Situating AI (and Big Data) in the Learning Sciences: Moving toward large-scale learning sciences 20. Linking Natural Language Use and Science Performance Section V: Other Topics in AI and STEM Education 21. Quick Red Fox: An app supporting a new paradigm in qualitative research on AIED for STEM 22. A systematic review of AI applications in computer-supported collaborative learning in STEM education 23. Inclusion and equity as a paradigm shift for artificial intelligence in education