用可解释AI辅助编程教学，让反馈既准确又可靠。
An Explainable AI Assistant for Introductory Programming Education: Improving Feedback Reliability with Instructor-AI Collaboration
- 结合教师经验构建可解释模型，精准定位代码错误
- 反馈经教师验证，准确率高且学生体验良好
- 适合教育科技、AI助教研究者参考
主动学习被广泛认为能有效提升入门编程课程的学习效果。然而，缺乏足够的教学支持常导致学生难以获得及时、个性化的反馈，而这对于掌握基础编程概念至关重要。尽管近年来人工智能（尤其是大语言模型）的发展为反馈提供了可扩展的解决方案，但可解释性与可靠性问题仍令人担忧。本文提出一个基于AI的课堂助教系统，利用可解释AI模型分析学生代码，将逻辑错误映射到教师识别的常见误解，并提供由教师编写的标准反馈，从而将可靠性建立在教师定义的教学知识基础上。为评估该框架的有效性，我们进行了专家评估以检验其与教师验证反馈的一致性，并在真实课堂中部署系统，调查学生对其可用性的感知。结果表明，该助手能够向学生提供准确且经教师验证的反馈，同时带来积极的学习体验。
原文摘要 · Abstract (English)
Active learning is widely recognized as an effective approach for improving learning outcomes in introductory programming courses. However, insufficient instructional support often limits students' access to timely, personalized feedback, which is crucial for mastering foundational programming concepts. Although recent advances in AI, particularly large language models, offer scalable opportunities for feedback, concerns about explainability and reliability remain. In this paper, we present an AI-driven classroom assistant that leverages an explainable AI model to analyze student code, map logical errors to instructor-identified misconceptions, and deliver instructor-authored feedback, thereby grounding reliability in instructor-defined pedagogical knowledge. To evaluate the effectiveness of our framework, we conducted an expert evaluation to examine its alignment with instructor-verified feedback and deployed the system in a classroom setting to assess students' perceptions of its usability. Results indicate that the assistant can provide accurate, instructor-verified feedback to students while fostering a positive experience.
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