将AI助教嵌入Moodle系统，实现代码作业的智能反馈与学习能力培养。
Design of AI-Powered Tool for Self-Regulation Support in Programming Education
- 基于LLM构建集成于Moodle的编程助教，结合课程内容与学生作答生成上下文感知反馈。
- 通过策略性AI回复帮助学生发展自我调节学习能力，而不仅关注知识获取。
- 适合教育科技开发者与编程教学研究者，推动个性化学习支持系统落地。
大型语言模型（LLM）工具已证明能提供即时、个性化的反馈，对有效编程教育至关重要。然而，许多此类工具独立于机构学习管理系统运行，造成信息割裂，难以利用学习资料和题目上下文生成精准反馈。此外，现有自调节学习与LLM支持的研究多聚焦知识掌握，忽视了自调节技能的发展。为此，我们开发了CodeRunner Agent，一个基于LLM的编程助教，整合Moodle中的CodeRunner插件（用于提交代码执行与自动评分）。该系统使教师能够根据课程材料、编程题、学生答案及执行结果，定制化生成高情境敏感性的反馈。同时，它通过策略性AI响应提升学生的自调节学习能力。这一融合上下文感知与技能导向的集成方法，为数据驱动的编程教育改进提供了新路径。
原文摘要 · Abstract (English)
Large Language Model (LLM) tools have demonstrated their potential to deliver high-quality assistance by providing instant, personalized feedback that is crucial for effective programming education. However, many of these tools operate independently from institutional Learning Management Systems, which creates a significant disconnect. This isolation limits the ability to leverage learning materials and exercise context for generating tailored, context-aware feedback. Furthermore, previous research on self-regulated learning and LLM support mainly focused on knowledge acquisition, not the development of important self-regulation skills. To address these challenges, we developed CodeRunner Agent, an LLM-based programming assistant that integrates the CodeRunner, a student-submitted code executing and automated grading plugin in Moodle. CodeRunner Agent empowers educators to customize AI-generated feedback by incorporating detailed context from lecture materials, programming questions, student answers, and execution results. Additionally, it enhances students' self-regulated learning by providing strategy-based AI responses. This integrated, context-aware, and skill-focused approach offers promising avenues for data-driven improvements in programming education.
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