How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866909343664832512 |
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| author | Ma, Qianou Shen, Hua Koedinger, Kenneth Wu, Tongshuang |
| author_facet | Ma, Qianou Shen, Hua Koedinger, Kenneth Wu, Tongshuang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_05292 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging Ma, Qianou Shen, Hua Koedinger, Kenneth Wu, Tongshuang Human-Computer Interaction Software Engineering 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. |
| title | How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging |
| topic | Human-Computer Interaction Software Engineering |
| url | https://arxiv.org/abs/2310.05292 |