TeachLM: Post-Training LLMs for Education Using Authentic Learning Data

• URL: http://arxiv.org/abs/2510.05087v1
• Date: Mon, 06 Oct 2025 17:55:04 GMT
• Title: TeachLM: Post-Training LLMs for Education Using Authentic Learning Data
• Authors: Janos Perczel, Jin Chow, Dorottya Demszky,
• Abstract summary: TeachLM is a large language model optimized for teaching using parameter-efficient fine-tuning of state-of-the-art models.<n>We use parameter-efficient fine-tuning to develop an authentic student model that enables the generation of high-fidelity synthetic student-tutor dialogues.<n>Our evaluations demonstrate that fine-tuning on authentic learning data significantly improves conversational and pedagogical performance.
• Score: 4.600044635815686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The promise of generative AI to revolutionize education is constrained by the pedagogical limits of large language models (LLMs). A major issue is the lack of access to high-quality training data that reflect the learning of actual students. Prompt engineering has emerged as a stopgap, but the ability of prompts to encode complex pedagogical strategies in rule-based natural language is inherently limited. To address this gap we introduce TeachLM - an LLM optimized for teaching through parameter-efficient fine-tuning of state-of-the-art models. TeachLM is trained on a dataset comprised of 100,000 hours of one-on-one, longitudinal student-tutor interactions maintained by Polygence, which underwent a rigorous anonymization process to protect privacy. We use parameter-efficient fine-tuning to develop an authentic student model that enables the generation of high-fidelity synthetic student-tutor dialogues. Building on this capability, we propose a novel multi-turn evaluation protocol that leverages synthetic dialogue generation to provide fast, scalable, and reproducible assessments of the dialogical capabilities of LLMs. Our evaluations demonstrate that fine-tuning on authentic learning data significantly improves conversational and pedagogical performance - doubling student talk time, improving questioning style, increasing dialogue turns by 50%, and greater personalization of instruction.

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