Integrating Dynamic Supports into an Equity Teaching Simulation to Promote Equity Mindsets

Abstract

Implementing high-quality professional learning on diversity, equity, and inclusion (DEI) issues is a massive scaling challenge. Integrating dynamic support using natural language processing (NLP) into equity teaching simulations may allow for more responsive, personalized training in this field. In this study, we trained machine learning models on participants' text responses in an equity teaching simulation (494 users; 988 responses) to detect certain text features related to equity. We then integrated these models into the simulation to provide dynamic supports to users during the simulation. In a pilot study (N = 13), we found users largely thought the feedback was accurate and incorporated the feedback in subsequent simulation responses. Future work will explore replicating these results with larger and more representative samples.