Exploring Teacher-AI Collaboration and Its Implications on Science Instruction

• In Event: Exploring Synergies: Teacher-AI Collaboration in K-12 STEM

Abstract

This study explores the role of AI assistance in science education, specifically examining how experienced high school science teachers can leverage AI to enhance science instruction. Through contextual inquiry sessions, we investigated teachers' workflows with varying degrees of AI engagement and their perceptions of AI-generated materials. The study highlights four key collaboration practices—orientation, evaluation, elaboration, and exploration—demonstrating how teacher-AI collaboration can support diverse teaching practices in science. Furthermore, findings indicate that teachers view AI as valuable for enhancing instructional goals, including personalized student learning, lesson preparation, formative assessment, and meaningful classroom discourse. We conclude by recommending that future studies focus on balancing support for teachers, students, and the discipline of science to ensure that generative AI enhances the learning experience.

Authors

• Soobin Jeon, University of Michigan

• Yunyan Li, University of Michigan

• Xingjian Gu, University of Michigan

• Xinyi Lu, University of Michigan

• Xiaoyu Liu, University of Maryland

• Joshua Littenberg-Tobias, GBH

• Shawn Stevens, WGBH

• Xu Wang, University of Michigan

• Ying Xu, Harvard University