Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education

2024/11/04 by Alexandra Vassar, Vassar, Alexandra, J. Renzella +5
Computer Science · #Computation and Language (cs.CL) #Digital Rights Management and Security #FOS: Computer and information sciences #Mathematics, Computing, and Information Processing #Open Education and E-Learning

paper · pdf · doi:10.48550/arxiv.2411.01765

openalex publication_date 2024/11/04 · openalex created_date 2024/11/14 · openalex updated_date 2026/07/28

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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