How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging

• URL: http://arxiv.org/abs/2310.05292v5
• Date: Thu, 10 Oct 2024 16:29:59 GMT
• Title: How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging
• Authors: Qianou Ma, Hua Shen, Kenneth Koedinger, Tongshuang Wu,
• Abstract summary: Large Language Models (LLMs) now excel at generative skills and can create content at impeccable speeds. Human novices play the role of Teaching Assistants and help LLM-powered teachable agents code. We introduce Hypo, a novel system to facilitate deliberate practice on debug, where human novices play the role of Teaching Assistants and help LLM-powered teachable agents code.
• Score: 28.321080454393687
• License:
• Abstract: Large Language Models (LLMs) now excel at generative skills and can create content at impeccable speeds. However, they are imperfect and still make various mistakes. In a Computer Science education context, as these models are widely recognized as "AI pair programmers," it becomes increasingly important to train students on evaluating and debugging the LLM-generated code. In this work, we introduce HypoCompass, a novel system to facilitate deliberate practice on debugging, where human novices play the role of Teaching Assistants and help LLM-powered teachable agents debug code. We enable effective task delegation between students and LLMs in this learning-by-teaching environment: students focus on hypothesizing the cause of code errors, while adjacent skills like code completion are offloaded to LLM-agents. Our evaluations demonstrate that HypoCompass generates high-quality training materials (e.g., bugs and fixes), outperforming human counterparts fourfold in efficiency, and significantly improves student performance on debugging by 12% in the pre-to-post test.

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