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


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

Academic Affiliations


  •  Ph.D. in Curriculum and Instruction (physics), 2017
    Beijing Normal University


 Aderhold Hall, 105J
 706-542-4548 (office)

Research Summary

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 in high-impact journals such as Journal of Research in Science Teaching, Studies in Science Education, International Journal of Science Education, Journal of Science Education and Technology, Research in Science Education, Computers& Education, British Journal of Educational Technology, International Journal of Educational Research, Studies in Educational Evaluation, etc.

I served as the Guest Editor of a Special Issue in the Journal of Science Education and Technology: Applying Machine Learning in Science Assessment: Opportunity and Challenge. I am serving on the Editorial Board: Journal of Research in Science Teaching, Journal of Science Education and Technology, and Disciplinary and Interdisciplinary Science Education Research.


Lead PI: Collaborative Research: Supporting Instructional Decision Making: The Potential of An Automatically Scored Three-dimensional Assessment System (PASTA). National Science Foundation, DRK-12, $2,999,996.

This is collaborative research in four institutions. The project is sponsored by NSF with a total amount of $2,999,996 for four years. The Lead PI, Xiaoming Zhai at the University of Georgia, has the Award ID: 2101104 ($903,421). The PIs in other institutions are Joe Krajcik ($ 890,000), Yue Yin ($ 596,575), Gary Weiser ($ 610,000).

Any questions or inquiries for this project should direct to Dr. Xiaoming Zhai at

Co-PI: Collaborative Research: ​ArguLex - Applying Automated Analysis to a Learning Progression for Argumentation. ​National Science Foundation, EHR Core, Award ID: DUE-​ ​1561159.
PI: AI-based Assessment in STEM Education Conference: Potential, Challenge, and Future. Award ID: 2138854
PI: Affordable Course Materials Grant, 2020-2021, $5,000, University of Georgia


Zhai, X. & Jackson, F. D. (In press). A Pedagogical Framework for Mobile Learning in Science Education. In Tierney, R., Rizvi, F., Ercikan, K., & Smith, G. (Eds.), International Encyclopedia of Education (4th ed., Vol. The Rise of STEM Education. Liu, X., & Wang L. (Eds.)). Published by Elsevier.
Zhai, X. (2020). The strategy for teaching physics ideas. Beijing, CN: Beijing Normal University Press. (In Chinese)

Awards and Accolades

NAEd/Spencer Research Development Award

National Academy of Education, 2021

AERA SIG TACTL Early Career Award

American Educational Research Association, 2021

Jhumki Basu Scholar Award

National Association of Research in Science Teaching, 2020