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AI for STEM Education Research

The foci of the AI4STEM lab is to conduct research in applying Artificial Intelligence (AI) to STEM education. Our projects are funded by the National Science Foundation and NAEd/Spencer. We aim to enhance STEM education by increasing the realization of AI’s potential and feasibility as a means of scaffolding STEM teachers’ instructional decision-making and promoting students’ STEM learning performance.

Our research covers four strands:

(a) Conduct AI-augmented innovative and performance-based science assessment research by developing machine learning-based Next Generation Science Assessments. We have developed AI-based assessments to examine students’ scientific modeling competence, argumentation, and explanations, as well as science teachers’ PCK.

(b) Develop AI-based automatic scoring and feedback systems to support teachers’ instructional decision-making.

(c) Conduct research to examine AI scoring bias, especially focusing on students that are underrepresented in STEM.

(d) Develop curriculum to facilitate STEM students’ AI competence.

Our lab has developed an international and nationwide collaboration network. Our collaborators come from Michigan State University, the University of Illinois at Chicago, WestEd, University of Washington, Leibniz Institute for Science and Mathematics Education, etc.

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PASTAThis project studies the utility of a machine learning-based assessment system for supporting middle school science teachers in making instructional decisions.
ArguLexThis research project addresses measuring argumentation learning progression using machine learning to automate the scoring of student written work.
AI BiasThis project conducts a critical examination of scoring bias of machine learning algorithms on scoring students from underrepresented groups in STEM.

Principal Investigator

Xiaoming Zhai

Principal investigator Xiaoming Zhai is an assistant professor of science education in the Mary Frances Early College of Education and the Institute for Artificial Intelligence at the University of Georgia.

He is interested in developing innovative assessments (e.g., AI-based) and supporting teachers in implementing the assessments.

Contact Info

Xiaoming Zhai
110 Carlton Street Athens, GA 30602
© University of Georgia, Athens, GA 30602