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

Interests

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

Academic Affiliations

Education

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

Contact

 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.

I co-founded RAISE (i.e., Research in Artificial Intelligence-Involved Science Education) Research Interest Group of NARST, a global organization for improving science education through research, and am serving as the founding Chair. I am also Co-Chairing NARST Strand 1. Science Learning: Development of Student Understanding.

I am accepting talented and dedicated doctoral students and visiting scholars who are interested in innovative assessment practices.

Grants

Lead PI: Collaborative Research: Supporting Instructional Decision Making: The Potential of An Automatically Scored Three-dimensional Assessment System (PASTA). National Science Foundation, DRK-12, (Partners include MSU, UIC, and WestEd).
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. National Science Foundation, Award ID: 2138854
PI: Affordable Course Materials Grant, 2020-2021, University of Georgia
PI: State-of-the-Art Conference Grant, University of Georgia

Publications

Wilson, C., Haudek, K., Osborne, J., Stuhlsatz, M., Cheuk, T., Donovan, B., Bracey, Z., Mercado, M., & Zhai, X. (accepted pending revisions). Using automated analysis to assess middle school students’ competence with scientific argumentation. Journal of Research in Science Teaching.
Zhai, X., Haudek, K., &; Ma, W. (accepted pending revisions). Assessing argumentation using machine learning and cognitive diagnostic modeling. Research in Science Education.
Zhai, X., He, P., & Krajcik, J. (2022). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching. DOI:10.1002/tea.21773
Zhai, X. & Pellegrino, J. W. (Forthcoming). Large-Scale Assessment in Science Education. Handbook of Research in Science Education (Vol. III.). Routledge.
Yin, Y., Khaleghi, S., Hadad., R., & Zhai., X. (2022). Developing effective and accessible activities to improve and assess computational thinking and engineering learning. Educational Technology Research and Development. https://doi.org/10.1007/s11423-022-10097-w
Zhai, X. (2022). Assessing high-school students’ modeling performance on Newtonian mechanics. Journal of Research in Science Teaching. DOI: 10.1002/tea.21758
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

Sarah H. Moss Fellowship

University of Georgia, 2022

Humboldt Researh Fellowship for Experienced Researchers

The Alexander von Humboldt Foundation, 2022

Provost's Interdisciplinary Pre-Seeding Award

University of Georgia, 2022

Provost’s Affordable Course Materials Award

University of Georgia, 2021

Provost’s State-of-the-Art Conference Award

University of Georgia, 2021

NAEd/Spencer Research Development Award

National Academy of Education, 2021

AERA SIG TACTL Early Career Scholar Award

American Educational Research Association, 2021

Jhumki Basu Scholar Award

National Association of Research in Science Teaching, 2020