Guiding Teacher Use of Artificial Intelligence–Based Classroom Assessment to Improve Instructional Decisions: A Conceptual Framework (Poster 5)

• In Event: AERA Poster Session Sixteen
  In Poster Session: Emerging Technology

Abstract

Artificial intelligence (AI), such as machine learning and data mining, are widely used to automatically score student responses to performance-based classroom assessment tasks and generate a dashboard to display the information on student performance. However, with such innovative technologies, teacher use of assessment data in classrooms to make timely instructional decisions is still challenging, especially in the U.S. Next Generation of Science Standards. Based on teacher cognition theory, this study articulates a conceptual framework to guide teachers' use of AI-based classroom assessment to improve their instructional decisions. The framework consists of four stages, including access to the AI system through professional learning support, review of automatic report dashboard, receiving AI-recommended instructional strategies, and making instructional decisions and taking actions.

Authors

• Peng He, Michigan State University
  Presenting Author

• Namsoo Shin, Michigan State University
  Presenting Author

• Xiaoming Zhai, University of Georgia
  Presenting Author

• Joseph S. Krajcik, Michigan State University
  Presenting Author