Skip to page content

Xiaoming Zhai

  • Assistant Professor, Department of Mathematics and Science Education

Portrait of Xiaoming Zhai

Contact Information

View/Download CV

Office Location

105J Aderhold Hall
110 Carlton Street
Athens, Georgia 30602


Affiliated Units

Areas of Expertise

  • Science education with a focus on physics
  • Assessment and measurement theory
  • Applied quantitative methods and data sciences in science education
  • Technology integration theory in science education

Research Interests

  • AI/Machine learning-based innovative assessment practices in science
  • Learning progression
  • Mobile learning in science
  • Science teacher education and career motivation

Programs of Study

Research Summary

My teaching experience shapes my research philosophy: I believe that understanding students is paramount to any pedagogical strategies to create effective, equitable, and inclusive science learning. However, teachers usually struggle with effective assessment practices, especially those targeting the NGSS learning goals. I thus focus my research on developing and using innovative assessment to help teachers better understand their students. My ultimate goal is improving equitable and inclusive science education via valid and innovative assessment so that all students can thrive in sciences. Based on this goal, my research interests focus on two areas: (a)The first looks at using innovative assessment to examine complex constructs in science learning and teaching as one means of supporting teachers, approaching the topic through a variety of methodologies and technologies such as machine learning. (b) My second research area of interest concerns the application of assessment results in science teaching and learning. My research has broadly appeared on high-impact journals such as Journal of Research in Science Teaching (JRST), Studies in Science Education, International Journal of Science Education, Journal of Science Education and Technology (JOST), Research in Science Education, Computers& Education, British Journal of Educational Technology, International Journal of Educational Research, Studies in Educational Evaluation, etc.

I am serving as the Guest Editor of a Special Issue of JOST: Applying Machine Learning in Science Assessment: Opportunity and Challenge. I am also serving on the Editorial Board of JRST and JOST.

Announcement: I am currently accepting doctoral students with an interest in applying machine learning/AI in science education. The candidates are expected to take courses both in science education and computer science.


I have more than 10 years of experience teaching at the K-12 level and teacher preparation both in the US and China, along with significant experience conducting research with direct classroom application.

My extensive experience with classroom teaching both as a teacher and mentor informs my life-long career goal—developing equitable and inclusive science teaching for every student. I saw first-hand the discrepancy in outcomes for students based on gender or socioeconomic status, and how science teachers struggled to address these issues. This motivated me to continue my education so that I could further explore science teaching and learning and offer help to more science teachers to assist them with their instructional practices. I completed my doctoral study in the physics education group at Beijing Normal University, China's top educational university specializing in teacher preparation, and the quantitative research methods group at University of Washington. I received substantial training in the design of inclusive physics curriculum materials, science teacher preparation, and development and use of innovative assessment in my Ph.D. program, as well as in which where I was a Postdoctoral Research Associate at Stanford University (2017-2018) and Michigan State University (2019-2020), and a Visiting Scholar/Research Specialist at University of Illinois at Chicago (2018-2019). My learning experience has helped me support teachers in developing equitable and inclusive science teaching—thereby giving all students, regardless their culture, gender, or socioeconomic status, greater opportunities for success in science.


Degree Concentration Institution Year
Ph.D. Curriculum and Instruction (physics) Beijing Normal University 2017