Recent advances in the capabilities of conversational agents based on large language models make them a very promising tool for role playing K-12 students in order to train educators in conversational teaching practices, such as eliciting student thinking, explaining disciplinary content, and facilitating a classroom discussion. In fact, such simulations can and have been developed relatively quickly and without data to machine-learn from – neither classroom data nor human-simulated data. To enhance the usefulness and effectiveness of such teaching simulations, it is necessary to provide pedagogically sound, timely, and personalized feedback to the educator about their simulation performance. In this study, we present experiments on fine-tuning models to evaluate educator performance in an elicitation teaching simulation. The models are developed with data collected during usability testing of the simulation and evaluated on real user data. We show that even with relatively little fine-tuning data, robust performance can be obtained

We present a case study describing our efforts to integrate the open source GECToR code and models into our production NLP pipeline that powers many of Educational Testing Service’s products and prototypes. The paper’s contributions includes a discussion of the issues we encountered during integration and our solutions, the overarching lessons we learned about integrating open source projects, and, last but not least, the open source contributions we made as part of the journey.