AI 中文总结
该研究开发了支持课程特定标准的CodeStylist工具，用于为早期本科编程学习者提供本地化代码风格反馈，专家评审显示其有应用前景但存在信任度不足等问题，需优化工具设计。
AI 中文摘要
这篇创新实践全文介绍了CodeStylist，一款为早期本科编程课程提供课程标准感知型代码风格反馈的网页应用。CodeStylist解决了一个常见的教学缺口：要求学生遵循本地命名、格式、注释、组织和可读性约定，但对这些要求的反馈往往滞后或不一致。与通用代码检查工具或通用大语言模型（LLM）提示不同，CodeStylist支持课程特定标准、多文件提交以及文件和行级别的本地化解释，旨在指导修订而非评判正确性。我们报告了一项针对某早期本科编程课程18名教学人员的形成性专家评审。参与者使用自选代码工件探索原型，并完成了关于响应质量、预期学生使用情况和重新设计优先级的调查。评分显示感知实用性适中但信任度有限：感知正确性平均为60.7%，响应帮助度平均为3.50/5，响应有用性平均为3.33/5，预期学生学习效果平均为2.61/5。尽管存在这些担忧，18名受访者中有17名预计学生主要会将该工具用于风格检查，通常至少每周使用一次。开放式反馈显示，受访者重视CodeStylist能让隐性课程标准更清晰，但也担忧输出不可靠、过度依赖以及延迟或成本问题。我们将这些发现解读为：课程感知型风格反馈作为提交前修订辅助工具具有应用前景，但未来版本应结合确定性规则检查与LLM生成的解释、规则引用以及更强的验证支持。
英文摘要
This innovative practice full paper presents CodeStylist, a web application that provides course-standard-aware code style feedback for early undergraduate programming courses. CodeStylist addresses a common instructional gap: students are expected to follow local conventions for naming, formatting, comments, organization, and readability, but feedback on these expectations is often delayed or inconsistent. Unlike generic linters or general-purpose LLM prompts, CodeStylist supports course-specific standards, multi-file submissions, and file- and line-localized explanations intended to guide revision rather than grade correctness. We report a formative expert review with 18 instructional staff from one early undergraduate programming course. Participants explored the prototype using self-selected code artifacts and completed a survey about response quality, anticipated student use, and redesign priorities. Ratings indicated modest perceived utility but limited trust: perceived correctness averaged 60.7%, response helpfulness averaged 3.50/5, response usefulness averaged 3.33/5, and anticipated student learning averaged 2.61/5. Despite these concerns, 17/18 respondents expected students to use the tool primarily for style checking, often at least weekly. Open-ended feedback showed that respondents valued CodeStylist for making implicit course standards more visible, but were concerned about unreliable output, overreliance, and latency or cost. We interpret these findings as evidence that course-aware style feedback is promising as a pre-submission revision aid, but that future versions should combine deterministic rule checks with LLM-generated explanations, rule citations, and stronger verification support.
Comments9 pages, 1 table, 1 figure, accepted for publication at Frontiers in Education 2026
