发表机构
University of Auckland; University of New South Wales（奥克兰大学; 新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人工智能代码工具融入编程环境下学生代码生成规范，通过分析四年数据集，引入涵盖注释类型、代码表达水平和代码结构的分类法，用自动分类研究维度变化及学生反思，发现学生注释特点及关注点。
AI 中文摘要
随着人工智能代码工具集成到编程环境中，学生越来越多地用自然语言描述预期行为并依靠这些工具生成代码，重点从代码编写转向规范制定。然而，对于学生在人工智能辅助编程任务中作为规范编写的注释了解甚少。我们分析了一个四年的本科生编程提交和反思数据集，学生在其中编写注释以指导代码生成并使用测试用例反馈完善解决方案。我们引入了一个涵盖三个维度的分类法：注释类型、代码表达水平和代码结构。通过自动分类我们研究了这些维度在不同尝试中的变化以及学生在反思中如何描述这个过程。我们的发现表明，学生大多编写自然语言的‘是什么’注释，对于更多过程性结构转向‘怎么做’注释，并且更多地关注验证生成的代码而非反复重写注释。
英文摘要
As AI code tools become integrated into programming environments, students increasingly describe intended behavior in natural language and rely on these tools to generate code, shifting emphasis from code writing to specification. Yet little is known about the comments students write as specifications in AI-assisted programming tasks. We analyze a four-year dataset of undergraduate programming submissions and reflections from tasks in which students wrote comments to guide code generation and refined solutions using test-case feedback. We introduce a taxonomy spanning three dimensions: comment type, code expression level, and code construct. Using automated classification, we examine how these dimensions vary across attempts and how students describe the process in their reflections. Our findings show that students mostly wrote natural-language What comments, shifted toward How comments for more procedural constructs, and focused more on verifying generated code than on repeatedly rewriting comments.
Comments7 pages, 2 figures, 2 tables. Accepted in the Proceedings of the 2nd ACM Virtual Global Computing Education Conference (SIGCSE Virtual 2026)
