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

July 06, 2025 Β· Declared Dead Β· πŸ› Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

πŸ‘» CAUSE OF DEATH: Ghosted
No code link whatsoever

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Runcong Zhao, Artem Bobrov, Jiazheng Li, Cesare Aloisi, Yulan He arXiv ID 2507.04295 Category cs.CY: Computers & Society Cross-listed cs.AI, cs.CL, cs.HC Citations 1 Venue Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations Last Checked 5 months ago
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.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

πŸ“œ Similar Papers

In the same crypt β€” Computers & Society

R.I.P. πŸ‘» Ghosted

Green AI

Roy Schwartz, Jesse Dodge, ... (+2 more)

cs.CY πŸ› arXiv πŸ“š 1.5K cites 7 years ago

Died the same way β€” πŸ‘» Ghosted