TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors

2026-03-18

AI Summary

This study addresses the urgent need for scalable, pedagogically grounded real-time instructional coaching for university instructors. To this end, it proposes the first conversational coaching system that integrates educational principles with synthetic dialogue data: pedagogical rules are extracted from educational resources to generate high-quality synthetic teaching dialogues, which are then used to fine-tune a specialized large language model capable of identifying, diagnosing, and recommending strategies for instructors’ teaching challenges. This approach overcomes the limitations of general-purpose models in capturing instructional depth. Expert evaluations demonstrate that the system’s guidance is clearer, more reflective, and more responsive than that of GPT-4o mini, while user studies reveal a trade-off between dialogue depth and interaction efficiency.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• instructional guidance
• higher education instructors
• pedagogical support
• scalable teaching support
• professional development

Innovation

• pedagogically grounded chatbot
• synthetic dialogue generation
• fine-tuned language model
• instructional guidance
• data-centric pipeline