用大模型生成符合课程的个性化科学学习反馈，减轻教师负担。
LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
- 基于大模型构建三模块系统，精准捕捉学生推理错误。
- 用主题关联记忆链替代相似度检索，提升反馈相关性与准确性。
- 教师可介入调整，适合教育科技研究与教学实践者使用。
有效的反馈对学生成长至关重要，但传统方式耗时耗力。本文提出LearnLens，一个模块化的大语言模型驱动系统，用于生成科学教育中的个性化、课程对齐反馈。该系统包含三个部分：（1）误差感知评估模块，能捕捉复杂的推理错误；（2）课程锚定生成模块，采用结构化主题关联的记忆链机制，而非传统相似度检索，显著提升反馈的相关性并减少噪声；（3）教师参与式交互界面，支持定制与监督。该系统解决了现有系统在可扩展性与反馈质量上的关键挑战，为教师和学生提供高效、高质量的反馈支持。
原文摘要 · Abstract (English)
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.
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