How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging
arxiv(2023)
摘要
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
pre-to-post test.
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