LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop

TL;DR

LearnLens is a modular system leveraging large language models to generate personalized, curriculum-aligned feedback in science education, incorporating educator oversight and a structured memory chain for relevance.

Contribution

It introduces a novel curriculum-grounded generation approach with an educator-in-the-loop interface, improving feedback relevance and quality over traditional methods.

Findings

Effective in capturing nuanced reasoning errors

Provides scalable, high-quality feedback

Empowers educators with customization tools

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.