Title: Interleaving Natural Language Prompting with Code Editing for Solving Programming Tasks with Generative AI Models Show Chinese title

Title: 将自然语言提示与代码编辑相结合，用于生成式AI模型解决编程任务

Abstract: Nowadays, computing students often rely on both natural-language prompting and manual code editing to solve programming tasks. Yet we still lack a clear understanding of how these two modes are combined in practice, and how their usage varies with task complexity and student ability. In this paper, we investigate this through a large-scale study in an introductory programming course, collecting 13,305 interactions from 355 students during a three-day laboratory activity. Our analysis shows that students primarily use prompting to generate initial solutions, and then often enter short edit-run loops to refine their code following a failed execution. We find that manual editing becomes more frequent as task complexity increases, but most edits remain concise, with many affecting a single line of code. Higher-performing students tend to succeed using prompting alone, while lower-performing students rely more on edits. Student reflections confirm that prompting is helpful for structuring solutions, editing is effective for making targeted corrections, while both are useful for learning. These findings highlight the role of manual editing as a deliberate last-mile repair strategy, complementing prompting in AI-assisted programming workflows. Show Chinese abstract

Abstract: 如今，计算机专业的学生通常依赖自然语言提示和手动代码编辑来解决编程任务。 然而，我们仍然缺乏对这两种模式在实际中如何结合以及它们的使用方式如何随任务复杂度和学生能力变化的清晰理解。 在本文中，我们通过一项在入门编程课程中的大规模研究来探讨这一问题，在三天的实验活动期间收集了355名学生的13,305次交互。 我们的分析显示，学生主要使用提示生成初始解决方案，然后经常进入简短的编辑-运行循环，以在执行失败后改进他们的代码。 我们发现，随着任务复杂性的增加，手动编辑变得更加频繁，但大多数编辑仍然简洁，许多只影响一行代码。 表现较好的学生往往仅通过提示就能成功，而表现较差的学生则更多依赖编辑。 学生的反思确认，提示有助于构建解决方案，编辑对于进行有针对性的更正有效，而两者对于学习都有帮助。 这些发现突显了手动编辑作为有意识的最后一公里修复策略的作用，补充了提示在AI辅助编程工作流中的作用。