Fine-Tuning Large Language Models for Codebook-Guided Coding of Students' Mathematics Metaphor Responses

Student-generated metaphors about mathematics can reveal students' attitudes, beliefs, identities, and experiences, but human expert coding of these thematically and semantically complex open-ended responses is time-intensive and difficult to scale. This study examines whether LoRA-based supervised fine-tuning of large language models (LLMs) can improve their performance on codebook-guided coding tasks for student mathematics metaphors. We used a human-coded corpus of 2,265 Grade 6-8 responses t

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ethics.ai (10 August 2026), “Fine-Tuning Large Language Models for Codebook-Guided Coding of Students' Mathematics Metaphor Responses,” evidence record 18676, https://ethics.ai/record/18676 (originally published by arXiv cs.HC).

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