LearnLens：基于大语言模型的个性化、课程对齐反馈系统，教育者参与其中
LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
AI总结：
LearnLens是一个模块化LLM系统，通过错误感知评估、课程记忆链生成及教育者参与界面，提供个性化课程对齐反馈，提升科学教育反馈质量与可扩展性。
摘要：
有效的反馈对学生学习至关重要，但对教师而言却耗时费力。我们提出LearnLens，一个模块化的、基于大语言模型的系统，用于生成科学教育中个性化且与课程对齐的反馈。LearnLens包含三个组件：（1）一个错误感知的评估模块，能够捕捉细微的推理错误；（2）一个基于课程约束的生成模块，使用结构化的、主题关联的记忆链而非传统的基于相似性的检索，从而提升相关性并减少噪声；（3）一个教育者参与其中的界面，用于定制化与监督。LearnLens解决了现有系统中的关键挑战，提供可扩展的高质量反馈，赋能教师与学生。
英文摘要：
Effective feedback is essential for student learning but is time-intensive for teachers. We present LearnLens, a modular, LLM-based system that generates personalised, curriculum-aligned feedback in science education. LearnLens comprises three components: (1) an error-aware assessment module that captures nuanced reasoning errors; (2) a curriculum-grounded generation module that uses a structured, topic-linked memory chain rather than traditional similarity-based retrieval, improving relevance and reducing noise; and (3) an educator-in-the-loop interface for customisation and oversight. LearnLens addresses key challenges in existing systems, offering scalable, high-quality feedback that empowers both teachers and students.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
