用AI自动生成代码理解题，帮学生查漏补缺。
AutoMCQ -- Automatically Generate Code Comprehension Questions using GenAI
- 用GenAI自动构造多选题，替代人工出题。
- 集成到CodeRunner平台，支持批量评估。
- 适合教师快速检测学生代码理解程度。
学生常不真正理解自己编写的代码，这种问题往往在教育后期才显现，修复难度加大。随着生成式人工智能（GenAI）工具（如GitHub Copilot）普及，代码理解能力愈发重要。代码理解题能有效评估学习者掌握情况，并辅助检测抄袭。但传统方法耗时费力，难以规模化。本文提出AutoMCQ，利用GenAI自动构建多选题，集成于CodeRunner自动化评测平台，实现高效、可扩展的代码理解评估。
原文摘要 · Abstract (English)
Students often do not fully understand the code they have written. This sometimes does not become evident until later in their education, which can mean it is harder to fix their incorrect knowledge or misunderstandings. In addition, being able to fully understand code is increasingly important in a world where students have access to generative artificial intelligence (GenAI) tools, such as GitHub Copilot. One effective solution is to utilise code comprehension questions, where a marker asks questions about a submission to gauge understanding, this can also have the side effect of helping to detect plagiarism. However, this approach is time consuming and can be difficult and/or expensive to scale. This paper introduces AutoMCQ, which uses GenAI for the automatic generation of multiple-choice code comprehension questions. This is integrated with the CodeRunner automated assessment platform.
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