TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911528281702400 |
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| author | Molnar, Isabel Li, Peiyu Chen, Si Chawla, Sugana Lang, James Metoyer, Ronald Hua, Ting Chawla, Nitesh V. |
| author_facet | Molnar, Isabel Li, Peiyu Chen, Si Chawla, Sugana Lang, James Metoyer, Ronald Hua, Ting Chawla, Nitesh V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18189 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors Molnar, Isabel Li, Peiyu Chen, Si Chawla, Sugana Lang, James Metoyer, Ronald Hua, Ting Chawla, Nitesh V. Artificial Intelligence 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. |
| title | TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.18189 |