LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
This work addresses the need for scalable, high-quality feedback to empower teachers and students in education, though it appears incremental as it builds on existing LLM and feedback systems.
The paper tackled the problem of generating personalized, curriculum-aligned feedback in science education, which is time-intensive for teachers, by introducing LearnLens, an LLM-based system with an educator-in-the-loop interface, resulting in improved relevance and reduced noise in feedback generation.
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.