Higher education instructors often lack timely and pedagogically grounded support, as scalable instructional guidance remains limited and existing tools rely on generic chatbot advice or non-scalable teaching center human-human consultations. We present TeachingCoach, a pedagogically grounded chatbot designed to support instructor professional development through real-time, conversational guidance. TeachingCoach is built on a data-centric pipeline that extracts pedagogical rules from educational resources and uses synthetic dialogue generation to fine-tune a specialized language model that guides instructors through problem identification, diagnosis, and strategy development. Expert evaluations show TeachingCoach produces clearer, more reflective, and more responsive guidance than a GPT-4o mini baseline, while a user study with higher education instructors highlights trade-offs between conversational depth and interaction efficiency. Together, these results demonstrate that pedagogically grounded, synthetic data driven chatbots can improve instructional support and offer a scalable design approach for future instructional chatbot systems.
@article{arxiv.2603.18189,
title = {TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors},
author = {Isabel Molnar and Peiyu Li and Si Chen and Sugana Chawla and James Lang and Ronald Metoyer and Ting Hua and Nitesh V. Chawla},
journal= {arXiv preprint arXiv:2603.18189},
year = {2026}
}