Associate Professor, Department of Mathematics and Science Education Xiaoming Zhai investigates innovative science assessment and supports teachers in implementing assessment practices, with a current emphasis on applying AI and machine learning in science education. He serves as Principal Investigator on two NSF-funded projects and holds a Ph.D. in Curriculum and Instruction (physics) from Beijing Normal University (2017). Zhai is Guest Editor for a Special Issue of the Journal of Science Education and Technology on applying machine learning in science assessment and serves on the editorial boards of the Journal of Research in Science Teaching, Journal of Science Education and Technology, and Disciplinary and Interdisciplinary Science Education Research. His research addresses (a) the use of innovative assessments—including machine learning—to examine complex constructs in science learning and teaching and (b) the application of assessment results to instruction, with publications in journals such as Journal of Research in Science Teaching, Studies in Science Education, International Journal of Science Education, Research in Science Education, Computers & Education, and British Journal of Educational Technology. Education: Ph.D., Curriculum and Instruction (Physics), Beijing Normal University, 2017 Research Research Interests: AI/machine learning–based innovative assessment in science education Using assessments to examine complex constructs in science learning/teaching Applying assessment results to instruction Learning progression Mobile learning in science Science teacher education and career motivation
