2025
EMNLP
EMNLP 2025
LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
Abstract
AbstractEffective 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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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Natural Language Processing
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Keyword Pioneer
— error-aware assessment
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Human-AI Interaction
Natural Language Processing > Resources & Methods > Large Language Models
Artificial Intelligence > Core AI > Large Language Models
Natural Language Processing > Applications > Summarization
Interdisciplinary > Education
Artificial Intelligence > Core AI > Natural Language Generation