Recent developments in deep learning have resulted in code-generation models
that produce source code from natural language and code-based prompts with high
accuracy. This is likely to have profound effects in the classroom, where
novices learning to code can now use free tools to automatically suggest
solutions to programming exercises and assignments. However, little is
currently known about how novices interact with these tools in practice. We
present the first study that observes students at the introductory level using
one such code auto-generating tool, Github Copilot, on a typical introductory
programming (CS1) assignment. Through observations and interviews we explore
student perceptions of the benefits and pitfalls of this technology for
learning, present new observed interaction patterns, and discuss cognitive and
metacognitive difficulties faced by students. We consider design implications
of these findings, specifically in terms of how tools like Copilot can better
support and scaffold the novice programming experience.

近年来，深度学习的最新发展已经产生了从自然语言和代码提示中生成源代码的代码生成模型，在课堂上，学习编程的新手现在可以使用免费工具自动建议编程练习和作业的解决方案，然而，目前尚不清楚新手如何与这些工具实际交互。本文通过观察和采访初学者，研究了使用一种自动生成代码的工具 Github Copilot 的学生在典型的编程入门任务中的感知、交互模式和认知和元认知困难，并探讨 Copilot 等工具如何更好地支持和搭建新手编程体验的设计方案。