Evaluation of mathematical questioning strategies using data collected through weak supervision

High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies. However, a dialogue oriented open ended conversations like teaching a student about scale factor can be difficult to model. This paper presents a high-fidelity, AI based classroom simulator to help teachers rehearse research-based mathematical questioning skills. We take a human centered approach to designing our system relying advances in deep-learning, uncertainty quantification and natural language processing while acknowledging the limitations of conversational agents for specific pedagogical needs. Using experts' input directly during the simulation, we demonstrate how conversation success rate and high user satisfaction can be achieved.