用大模型为工程毕业设计周报提供个性化反馈，提升报告质量。
Automated Formative Feedback for Short-form Writing: An LLM-Driven Approach and Adoption Analysis
- 用大模型分析学生周报草稿，生成针对性改进建议。
- 使用该工具的学生报告完整性与质量显著提升。
- 适合教育科技研究者与教学改革实践者参考。
本文探讨了在工程毕业设计项目中，基于AI的形成性反馈系统在双周报告场景下的开发与应用。每位学生需撰写简短报告，总结过去两周的个人成果，由导师评估。为此，我们开发了一款基于大语言模型的工具，为学生提供个性化反馈，帮助其提升报告的完整性和质量。在两轮使用中，初始采纳率较低，存在使用障碍；但积极参与的学生能有效利用该工具，显著改善报告质量。此外，该工具的任务解析能力可识别潜在的学生任务与交付成果，为教学管理提供新视角。研究发现，尽管存在初期抵触情绪且采纳范围有限，但该系统展现出为师生提供有价值的形成性支持的潜力。
原文摘要 · Abstract (English)
This paper explores the development and adoption of AI-based formative feedback in the context of biweekly reports in an engineering Capstone program. Each student is required to write a short report detailing their individual accomplishments over the past two weeks, which is then assessed by their advising professor. An LLM-powered tool was developed to provide students with personalized feedback on their draft reports, guiding them toward improved completeness and quality. Usage data across two rounds revealed an initial barrier to adoption, with low engagement rates. However, students who engaged in the AI feedback system demonstrated the ability to use it effectively, leading to improvements in the completeness and quality of their reports. Furthermore, the tool's task-parsing capabilities provided a novel approach to identify potential student organizational tasks and deliverables. The findings suggest initial skepticism toward the tool with a limited adoption within the studied context, however, they also highlight the potential for AI-driven tools to provide students and professors valuable insights and formative support.
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