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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 

Role-plays of interpersonal interactions are essential to learning across professions, but effective simulations are difficult to create in typical learning management systems. To empower educators and researchers to advance simulation-based pedagogy, we have developed the Digital Clinical Simulation Suite (DCSS, pronounced "decks"), an open-source platform for rehearsing for improvisational interactions. Participants are immersed in vignettes of professional practice through video, images, and text, and they are called upon to improvisationally make difficult decisions through recorded audio and text. Tailored data displays support participant reflection, instructional facilitation, and educational research. DCSS is based on six design principles: 1) Community Adaptation, 2) Masked Technical Complexity, 3) Authenticity of Task, 4) Improvisational Voice, 5) Data Access through "5Rs", and 6) Extensible AI Coaching. These six principles mean that any educator should be able to create a scenario that learners should engage in authentic professional challenges using ordinary computing devices, and learners and educators should have access to data for reflection, facilitation, and development of AI tools for real-time feedback. In this paper, we describe the architecture of DCSS and illustrate its use and efficacy in cases from online courses, colleges of education, and K-12 schools. 

In this study, we examine the outcome of a four-day workshop with 24 Teacher Educators (fellows) who were supported in using two tools - Teacher Moments (TM) and Eliciting Learner Knowledge (ELK). The tools are designed for authoring, implementing, and research Digital Clinical Simulations in education. The simulations centered around issues of equity in K-12 computer science education to provide in-/pre-service teachers with opportunities to practice high-stakes interactions in low-stakes settings. We operationalize the technology adoption of the fellows through the notions of self-efficacy, help-seeking, and technology concerns to recognize the potential barriers they faced in transitioning from authoring to implementing and research design. Finally, we note the fellows' implementation plans in the ensuing academic year and examine potential collaborations amongst them using social network analysis. Our results reveal how a small group of fellows, spanning major regions of the U.S., generate a broad range of scenarios, as well as clusters of scenarios, enabling simulation-based research supported by collaboration.