LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
July 06, 2025 Β· Declared Dead Β· π Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
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Authors
Runcong Zhao, Artem Bobrov, Jiazheng Li, Cesare Aloisi, Yulan He
arXiv ID
2507.04295
Category
cs.CY: Computers & Society
Cross-listed
cs.AI,
cs.CL,
cs.HC
Citations
1
Venue
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Last Checked
5 months ago
Abstract
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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