AI For STEM Education Lab
AI4STEM Lab explores the great potential 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 on applying Artificial Intelligence (AI) to enhance STEM education. Our projects are funded by the National Science Foundation, National Institute of Health, NAEd/Spencer Foundation, and Alexander von Humboldt Foundation. 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
Projects
PASTA
This project studies the utility of a machine learning-based assessment system for supporting middle school science teachers in making instructional decisions.
ArguLex
This research project addresses measuring argumentation learning progression using machine learning to automate the scoring of student written work.
AI Bias
This project conducts a critical examination of scoring bias of machine learning algorithms on scoring students from underrepresented groups in STEM.
AI Conference
This conference funded by NSF will tackle the challenges of AI-based assessments in STEM education.
Latest News
The University of Georgia received a five-year, $10 million grant from the Institute of Education Sciences to establish a research and development center that will provide national leadership on best practices for using generative artificial intelligence (GenAI) in schools, strengthening competence in GenAI in middle school science classrooms.
In collaboration with Vanderbilt University, Educational Testing Service, and Albany State University, the National Center on Generative AI for Uplifting STEM+C Education (GENIUS Center) will facilitate the teaching and learning of science through development of GenAI learning agents to both improve competence in STEM subjects and demonstrate how to use GenAI tools responsibly.
Led by Xiaoming Zhai, an associate professor in the UGA Mary Frances Early College of Education, the GENIUS Center research team will conduct studies at middle schools in both urban and rural settings across five states to evaluate the current uses of AI in classrooms, as well as determine what features to incorporate into a GenAI learning agent. Findings will inform the development of a tool, called GenAgent, and conclude with a pilot test of GenAgent in middle school science classrooms.
Along with Zhai, UGA faculty involved in the project include Yizhu Gao, Lehong Shi, and Ehsan Latif in the College of Education; Tianming Liu and Ninghao Liu in the School of Computing; and Xianqiao Wang in the College of Engineering.
Check out the full story https://coe.uga.edu/news/2024-10-10-million-grant-to-fund-national-genai-center-at-uga-transform-middle-school-stem-education/
The AI4STEM Education Center at UGA Mary Francs Early College of Education is excited to announce that we’re looking for 2 Postdoctoral Research Associates to join our team! You’ll be working with Dr. Xiaoming Zhai and a dynamic team on NSF & NIH funded projects aimed at enhancing classroom assessment, addressing educational inequities, and improving data science education in K12 and higher ed.
If you have a Ph.D. in education, data science, or a computer science-related field and are passionate about transforming education through innovation, we encourage you to apply!
📌 Learn more and apply here: https://lnkd.in/etpm-Hgm
Check out the latest chapter by “AI and Machine Learning for Next Generation Science Assessments” in the new book Machine Learning, Natural Language Processing, and Psychometrics: New chapter.
The open-access journal Education Sciences (ISSN 2227-7102) is pleased to announce the launch of a new Special Issue titled “Generative AI in Education: Current Trends and Future Directions”. Dr. Xiaoming Zhai is serving as the Guest Editor for this issue.
We cordially invite scholars, researchers, and practitioners in the field of AI and education to contribute their expertise to this Special Issue. The aim is to explore cutting-edge research on the applications, impacts, and future potential of generative AI in educational settings.
For more information on the scope of this issue and submission guidelines, please visit the Special Issue website: Generative AI in Education.
Submission Details:
- Papers may be submitted now until 15 June 2025.
- Papers will be published on an ongoing basis, subject to acceptance after peer review.
- Submitted papers must be original and not under consideration elsewhere.
- Authors are encouraged to send a short abstract or tentative title in advance to the Editorial Office at: alex.zheng@mdpi.com.
We look forward to your contributions and hope this opportunity leads to a productive collaboration.
Congratulations to Matthew Nyaaba for being named the Amazing Student for September 2024 in the College of Education at the University of Georgia! (See the full post)
He, X., Chen, Y., Zhai, X., and Yin, Y. (2022). Reviewing automatically generated assessment reports for teachers’ formative uses. Paper presented at the International Conference for AI-based Assessments in STEM Education, Athens, Georgia. (Click Here for Presentation)
Latif, E., Zhai, X., He, X., & Amerman, H. (2022). AI-scorer: An AI-augmented teacher feedback platform. Paper presented at the International Conference for AI-based Assessments in STEM Education, Athens, Georgia. (Click Here for Presentation)
Panjwani, S. & Zhai, X. (2022). AI for students with learning disabilities: A systematic review. Paper presented at the International Conference for AI-based Assessments in STEM Education, Athens, Georgia. (Click Here for Presentation)
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 Behavior, 129. 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 Education, 6. 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, Principal Investigator of AI for STEM Education Lab
Dr. Zhai edited a Special Issue in Journal of Science Education and Technology, entitled Applying Machine Learning in Science Education
Partners
