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    <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.</abstract>
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%0 Conference Proceedings
%T LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
%A Zhao, Runcong
%A Bobrov, Artem
%A Li, Jiazheng
%A Aloisi, Cesare
%A He, Yulan
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F zhao-etal-2025-learnlens
%X 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.
%U https://aclanthology.org/2025.emnlp-demos.45/
%P 625-633

Markdown (Informal)

[LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop](https://aclanthology.org/2025.emnlp-demos.45/) (Zhao et al., EMNLP 2025)

• LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop (Zhao et al., EMNLP 2025)

ACL

• Runcong Zhao, Artem Bobrov, Jiazheng Li, Cesare Aloisi, and Yulan He. 2025. LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 625–633, Suzhou, China. Association for Computational Linguistics.