Optimizing Learning Support Design: Predictive Links to Teaching Knowledge and Self-Efficacy in AI-Integrated Virtual Reality

• In Event: AERA Roundtable Session Saturday 8:00 am Four Seasons Ballroom 1
  In Roundtable Session: Simulation-Based Design (Table 10)

Abstract

In this study, we explored the design of learning support and its predictive links to preservice teachers’ teaching knowledge and self-efficacy in a virtual reality teaching simulation with AI-powered virtual students. Nineteen preservice teachers were recruited for this cross-sectional quasi-experimental design study. They used learning support throughout the virtual teaching simulation session. We collected data via video-logged duration of learning support use as well as the post-intervention teaching knowledge-skill test and teaching self-efficacy survey. We found a significant predictive link between the duration of learning support use and teaching knowledge-skill test performance, with learning support used in the pretraining and that in the post-simulation stage yielding positive significant impacts. The impacts of learning support were not found on teaching self-efficacy.

Authors

• Chih-Pu Dai, Texas A&M University

• Fengfeng Ke, University of Maryland

• Nuodi Zhang, Florida State University