Preservice Teacher Learning in Virtual Reality Simulation with Artificial Intelligence-Powered Virtual Students: Emotions and Teacher Talk Patterns

Abstract

The purpose of this study was to explore preservice teachers’ emotions and dialogic teacher talks in a virtual reality (VR) simulation-based learning (SBL) environment with artificial intelligence (AI)-powered virtual students. We collected computer-logged natural language data with 22 preservice teachers in this exploratory case study. We employed a heuristic evaluation approach with data-mining techniques (i.e., association rule mining). The preliminary results suggested that the AI-powered virtual students designed in the VR SBL environment can 1) foster preservice teachers’ positive emotions while also challenging them to achieve their teaching goals; 2) support preservice teachers in enacting and sustaining productive and meaningful ambitious teaching practices. The results highlighted that AI-powered virtual students, designed in the SBL environment, emulate natural interactions to enhance preservice teachers’ enactive teaching practices, moving beyond merely presenting information thus increasing disciplinary-focused teaching enactments. The learning sciences community may refer to the findings to design SBL in teacher education.