@inproceedings{zhao-etal-2025-learnlens,
    title = "{L}earn{L}ens: {LLM}-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop",
    author = "Zhao, Runcong  and
      Bobrov, Artem  and
      Li, Jiazheng  and
      Aloisi, Cesare  and
      He, Yulan",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-demos.45/",
    pages = "625--633",
    ISBN = "979-8-89176-334-0",
    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."
}