用AI生成可操作的互评反馈，教师把关让评分更公平高效。
AICoFe: Implementation and Deployment of an AI-Based Collaborative Feedback System for Higher Education
- 多大模型协同分析评分与评语，生成结构化反馈
- 教师通过仪表板审核修改AI初稿，确保质量可控
- 适合教育技术研究者和高校教师提升互评效率
在高等教育中，有效的同伴反馈对培养批判性反思能力至关重要，但学生生成的评论质量参差不齐，限制了其实际影响。本文介绍了AICoFe（基于AI的协作反馈系统）的实现与部署，该系统通过以人为中心的AI方法弥补这一差距。我们提出一种模块化架构，协调多大语言模型（GPT-4.1-mini、Gemini 2.5 Flash、Llama 3.1）的流水线，将量化评分量表数据与定性观察融合为清晰、可操作的反馈。系统核心是“教师在环”调解流程，教育者使用专用学习分析仪表板对AI生成的草稿进行筛选与优化后才发布。此外，我们详述了底层数据基础设施，采用混合SQL与MongoDB策略，保障反馈过程的可追溯性，并管理半结构化反馈版本。
原文摘要 · Abstract (English)
Effective peer feedback is essential for developing critical reflection in higher education, yet its impact is often limited by the inconsistent quality of student-generated comments. This paper presents the implementation and deployment of AICoFe (AI-based Collaborative Feedback), a system designed to bridge this gap through a human-centered AI approach. We describe a modular architecture that orchestrates a multi-LLM pipeline, utilizing GPT-4.1-mini, Gemini 2.5 Flash, and Llama 3.1, to synthesize quantitative rubric data and qualitative observations into coherent, actionable feedback. Key to the system is a "teacher-in-the-loop" mediation workflow, where educators use specialized Learning Analytics dashboards to curate and refine AI-generated drafts before delivery. Furthermore, we detail the underlying data infrastructure, which employs a hybrid SQL and MongoDB strategy to ensure traceability and manage semi-structured feedback versions.
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