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.

翻译：有效的同伴反馈对于培养高等教育的批判性反思能力至关重要，但其效果常因学生评语质量参差不齐而受限。本文介绍了AICoFe（基于人工智能的协作反馈）系统的实现与部署，该系统通过以人为本的人工智能方法弥合这一差距。我们描述了一个协调多LLM流程的模块化架构，利用GPT-4.1-mini、Gemini 2.5 Flash和Llama 3.1，将定量评分量表数据与定性观察结果综合为连贯且可操作的反馈。系统的核心在于"教师介入"中介工作流——教育工作者通过专用学习分析仪表盘，在反馈提交前对AI生成的草稿进行筛选与润色。此外，我们详述了底层数据基础设施，该设施采用SQL与MongoDB混合策略，以确保可追溯性并管理半结构化反馈版本。