Improving mathematical questioning in teacher training

TL;DR

This paper presents a human-centered, AI-driven conversational agent designed to help teachers improve their mathematical questioning skills through simulated classroom interactions, leveraging recent advances in NLP and deep learning.

Contribution

It introduces a novel text-based interactive agent for teacher training in mathematical questioning, integrating expert input and addressing dialogue modeling challenges.

Findings

High conversation success rate achieved

Users reported high satisfaction

Effective integration of NLP and deep learning techniques

Abstract

High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies. However, dialogue-oriented open-ended conversations such as teaching a student about scale factors can be difficult to model. This paper builds a text-based interactive conversational agent to help teachers practice mathematical questioning skills based on the well-known Instructional Quality Assessment. We take a human-centered approach to designing our system, relying on 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.