Supporting Teacher Timely Instructional Decisions Using Artificial Intelligence-Assisted Information to Promote Student Science Learning

• In Event: Sunday Roundtable 9:45 am
  In Roundtable Session: Innovative Approaches to Applying Technology in Science Education Research (Table 31)

Abstract

Teacher instructional decisions are pivotal in improving student learning. Artificial Intelligence (AI) can significantly alleviate teacher workload by scoring student-constructed responses and providing timely feedback. This study explores what AI-generated information on assessment tasks teachers require to make timely instructional decisions that foster student learning in science. We collected interview data from three teachers in two schools who piloted our AI-assisted platform in science classes with 200 students. Study results revealed that to support teachers’ prompt instructional decision-making in classrooms, automatic reports need to include features for understanding student performances, supporting teaching, fostering student learning, and promoting classroom management for monitoring student engagement in assessment activities.

Authors

• Namsoo Shin, Michigan State University

• Katherine Joy Nilsen, WestEd

• Peng He, Washington State University

• Mao-Jen Tseng, Michigan State University

• Xinyu He, University of Georgia

• Joseph S. Krajcik, Michigan State University