AI For STEM Education Research

AI4STEM lab explores the great potentials of applying AI in STEM education through research and practice. We are committed to promoting the applications of AI in performance-based innovative assessment practices and teachers’ instructional decision making.

Photo by Alexander Supertramp

Research Foci

The focus of the AI4STEM lab is to conduct research in applying Artificial Intelligence (AI) to enhancing STEM education. Our projects are funded by 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. We are developing an automatic scoring and feedback system to grade students’ constructed responses to the Next Generation Science Assessments and provide teachers with PCK supports to scaffold their timely instructional decision-making. We are also concerned about AI scoring bias and we aim to develop a curriculum to improve AI competence for students that are underrepresented in STEM.

Photo by Atikah Akhtar on Unsplash



Latest News

Xiaoming Zhai, an assistant professor in the Department of Mathematics, Science, and Social Studies Education, recently co-founded a research interest group (RIG) with Kent Crippen, the Irving and Rose Fien Endowed Professor at the University of Florida. Housed in the National Association of Research in Science Teaching (NARST), the interest group is called Research in AI-Involved Science Education (RAISE). NARST is a global organization for improving science education through research. The NARST board of directors approved Zhai to serve as the founding chair of RAISE for three years.

RAISE aims at employing AI to extend the landscape of science education, increase the capacity of all participants in the venture to face worldwide challenges, and significantly address the equity and ethical problems in the world broadly. RAISE will (a) support cutting-edge innovations using AI to address learning, teaching, assessment, equity, and policy issues in science education; (b) communicate cutting-edge research involving AI to all researchers, practitioners, and policymakers; and (c) encourage junior scholars in the field to pursue AI innovations in science education research as it is broadly practiced.

Gao, Y., Cui, Y., Bulut, O., Zhai, X., & Chen, F. (2021). Examining adults’ web navigation patterns in multi-layered hypertext environments. Computers in Human Behavior129. https://doi.org/10.1016/j.chb.2021.107142 (Click Here for PDF)

Since the release of the Next Generation Science Standards in 2013, there was a call for developing knowledge-in-use assessments that integrate disciplinary core ideas and crosscutting concepts with science and engineering practices. This call requires a transformation from the traditional multiple-choice assessments to performance-based constructed responses to elicit students’ complex knowledge-in-use abilities. In this presentation, Dr. Zhai will present the innovative assessments that they developed and the approach of machine learning that they used to automatically grade students’ performance. He will introduce two studies applying machine learning to automatically assess students’ learning progression of argumentation and to automatically score students’ drawn models. He will conclude the talk by overviewing his current NSF project in terms of supporting teachers’ instructional decision making using automatically generated assessment reports.

This Research Topic specifically seeks contributions using Artificial Intelligence (AI) and machine learning, the most cutting-edge technology to tackle these educational challenges in STEM education.

Dr. Zhai presented the innovative assessments, titled Machine Learning-based Next Generation Science Assessments.

Gao, Y., Zhai, X., Cui, Y., Xin,T., & Bulut,O. (2021). Re-validating a Learning Progression of Buoyancy for Middle School Students: A Longitudinal Study. Research in Science Education. https://doi.org/10.1007/s11165-021-10021-x (Click Here for PDF)

Zhai, X., Haudek, K. C., Wilson, C., & Stuhlsatz, M. (2021). A Framework of Construct-Irrelevant Variance for Contextualized Constructed Response Assessment. Frontiers in Education6. https://doi.org/10.3389/feduc.2021.751283 (Click Here for PDF)


Highlight

Our team includes professors, graduate research assistants, and alumni. All of them are making great contribution to the field of AI for STEM education research!

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Collaborators

Assistant Professor, Principle Investigator of AI for STEM Education Research Lab

Dr. Zhai edited a Special Issue in Journal of Science Education and Technology, entitled Applying Machine Learning in Science Education

Article by Xiaoming Zhai featured on Wiley

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