Xiaoming Zhai

In the 21st century, the educational landscape is undergoing a seismic shift, with Artificial Intelligence (AI) emerging as a pivotal force reshaping the contours of teaching and learning, especially in the realm of science education. As educators, policymakers, and researchers grapple with the challenges and opportunities presented by this technological juggernaut, this project underscores the imperative to weave AI's transformative potential seamlessly with the foundational principles of Diversity, Equity, and Inclusion (DEI). The vision driving this initiative is twofold: harnessing the unparalleled capabilities of AI to revolutionize educational experiences while ensuring that these innovations are accessible, relevant, and beneficial to every student, irrespective of their background or circumstances.

The Framework for K-12 Science Education has set forth an ambitious vision for science learning by integrating disciplinary science ideas, scientific and engineering practices, and crosscutting concepts, so that students could develop competence to meet the STEM challenges of the 21st century. Achieving this vision requires transformation of assessment practices from relying on multiple-choice items to performance-based knowledge-in-use tasks. However, these performance-based constructed-response items often prohibit timely feedback, which, in turn, has hindered science teachers from using these assessments. Artificial Intelligence (AI) has demonstrated great potential to meet this assessment challenge. To tackle this challenge, experts in assessment, AI, and science education will gather for a two-day conference at University of Georgia to generate knowledge of integrating AI in science assessment.

This project will study the utility of a machine learning-based assessment system for supporting middle school science teachers in making instructional decisions based on automatically generated student reports (AutoRs). The assessments target three-dimensional (3D) science learning by requiring students to integrate scientific practices, crosscutting concepts, and disciplinary core ideas to make sense of phenomena or solve complex problems.