TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors
arXiv:2603.18189v1 Announce Type: new 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. Tog
arXiv:2603.18189v1 Announce Type: new 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.
Executive Summary
The article presents TeachingCoach, a fine-tuned scaffolding chatbot designed to provide instructional guidance to instructors in higher education. Built on a data-centric pipeline, TeachingCoach extracts pedagogical rules from educational resources and uses synthetic dialogue generation to guide 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. A user study highlights trade-offs between conversational depth and interaction efficiency, demonstrating TeachingCoach's potential as a scalable design approach for future instructional chatbot systems. While the results are promising, further research is needed to fully explore the limitations and potential applications of TeachingCoach.
Key Points
- ▸ TeachingCoach is a pedagogically grounded chatbot designed to support instructor professional development.
- ▸ TeachingCoach uses a data-centric pipeline to extract pedagogical rules from educational resources.
- ▸ Expert evaluations demonstrate TeachingCoach's effectiveness in producing clearer and more responsive guidance.
Merits
Strength in Pedagogical Grounding
TeachingCoach's design is grounded in pedagogical principles, making it a valuable resource for instructors seeking evidence-based guidance.
Fine-Tuned Scaffolding
TeachingCoach's use of synthetic dialogue generation enables fine-tuned scaffolding, allowing instructors to engage in targeted and effective professional development.
Demerits
Limited Generalizability
The study's focus on higher education instructors may limit the generalizability of TeachingCoach's effectiveness to other educational contexts.
Trade-Offs in Conversational Depth
The user study highlights trade-offs between conversational depth and interaction efficiency, suggesting that further research is needed to optimize TeachingCoach's design.
Expert Commentary
The article presents a promising approach to instructional support, leveraging AI-powered chatbots to provide pedagogically grounded guidance to instructors. While the study's findings are encouraging, further research is needed to fully explore the limitations and potential applications of TeachingCoach. In particular, the trade-offs between conversational depth and interaction efficiency highlight the need for ongoing design refinement. As educators and policymakers consider the integration of AI-powered instructional support into their practices, they would do well to prioritize the development of pedagogically grounded AI systems that can support teacher professional development and improve student outcomes.
Recommendations
- ✓ Further research should focus on refining TeachingCoach's design to optimize conversational depth and interaction efficiency.
- ✓ Policymakers and educators should prioritize the development of pedagogically grounded AI systems that can support teacher professional development and improve student outcomes.
