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arXiv · 2411.01765

Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education

Alexandra Vassar·Jake Renzella·Emily Ross·Andrew Taylor
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Abstract

This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education, addressing concerns that LLMs may hinder learning outcomes. The project utilised a proprietary dataset of 2,500 high quality question/answer pairs from programming course forums, and explores two research questions: the suitability of university course forums in contributing to fine-tuning datasets, and how supervised fine-tuning can improve LLMs' alignment with educational principles such as constructivism. Initial findings suggest benefits in pedagogical alignment of LLMs, with deeper evaluations required.

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Alexandra Vassar, Jake Renzella, Emily Ross, Andrew Taylor. 2024-11-04. Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education. https://arxiv.org/abs/2411.01765

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