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

Core Finding

LearnLens is a modular, LLM-based system that generates personalised, curriculum-aligned feedback in science education by combining error-aware assessment, a topic-linked memory chain, and an educator-in-the-loop interface. Zhao et al. (2025) report that this architecture produces high-quality scoring (MSE 3.19, 8–13% below any baseline) while keeping latency (~11.4s) and cost ($0.0099/request) competitive, and that teachers rated it strongly for usability, curriculum relevance (4.4/5), and scientific accuracy (4.3/5).

Three Core Components

LearnLens is designed as a dual-interface system serving both teachers and students, built around three components that each target a shortcoming of prior AI-feedback systems.

1. Error-Aware Assessment Module

The "Assessor" maps each student answer onto a curriculum-aligned mark scheme of weighted key concepts, awarding partial credit when a concept is detected even if the final answer is wrong. It decouples conceptual understanding from writing quality: grammatical and typographical issues are captured by a separate expression-quality flag and excluded from the numerical grade. A self-reflection mechanism is triggered when a direct "grade-this-answer" prompt score diverges from the concept-matching score, and the transparent weighting of concepts is said to ease disputes and remove the "black box" impression.

2. Curriculum-Grounded Generation (Chain-of-Concept)

Rather than similarity-based retrieval, the system organises past assessments by curriculum topics into a topic graph and confines retrieval to the topic subgraph for a query — an explicit departure from traditional RAG. The authors argue this reduces cross-topic noise and improves relevance, producing pedagogically coherent feedback aligned to learning objectives. A safety-aligned model filters harmful, biased, or otherwise inappropriate language post-generation.

3. Educator-in-the-Loop Interface

Teachers can monitor student performance, review and revise feedback through a conversational interface, propagate edits across an entire quiz, and select embedded verifiers that score feedback on Scientific Accuracy, Clarity (accessible to GCSE-level students), and Specificity. A verification-and-revision loop iterates until criteria meet a threshold or the iteration limit is reached. Importantly, teacher intervention is treated as a signal of dissatisfaction that triggers allocation of additional computational resources for deeper reflection — prioritising feedback quality and user trust over speed.

Evaluation Results

Teacher user study (N = 30, prior STEM teaching experience): Mean ratings never fell below 4.1/5 across nine Likert items; ~80% called the menu "intuitive"; 75% expect results in under 15 seconds (met in >90% of local-deployment cases). Median time per assignment dropped from 10–30 minutes to under 5 minutes. A χ² test (p = 0.63) showed no significant difference in adoption intent across prior-method groups, with every cohort exceeding 65% "continue using" (automation-savvy peak 85%).
Agent performance (100 authentic student answers): LearnLens scored best on MSE (3.190), correlation (0.388), exact (0.354), and ±1 (0.747) accuracy, while maintaining latency ~4× faster than QwQ-32B and cost 72% cheaper, via a modular pipeline that invokes lightweight models for routine subtasks and larger models only for deeper reasoning, supported by vLLM serving and speculative decoding.
Privacy: All experiments were conducted via local deployment due to student data privacy concerns (evaluated models: Llama-3-8B, Qwen2.5-32B, QwQ-32B).

Implications

This work illustrates how Curriculum Design can be operationalised inside an LLM-based feedback system, and how Human In The Loop AI oversight — via interactive teacher revision and embedded verifiers — can address the Hallucination Risk and lack-of-control criticisms levelled at purely automated feedback. The topic-linked memory chain is a direct contribution to reducing retrieval noise, engaging debates around RAG and Knowledge Graph organisation for Educational NLP. Its focus on STEM Education and GCSE science demonstrates the Teacher Role in validating AI output rather than being displaced by it, supporting AI Feedback Quality and Formative Assessment goals through an explicit Feedback Loop.

Connection to Existing Wiki

AI Feedback Quality: Provides a concrete verifier-based framework (accuracy, clarity, specificity) for assessing generated feedback quality.
Student Modeling: The error-aware assessment module captures partial understanding and reasoning errors rather than binary correctness, aligning with richer student modelling.
Feedback Loop: The verification-and-revision loop and educator-intervention-triggered deep reflection are core feedback-loop mechanisms.
Curriculum Design: Grounds feedback generation in national-curriculum-aligned mark schemes and topic structures.
RAG / Knowledge Graph: Directly challenges similarity-based retrieval with a topic-linked memory chain (Chain-of-Concept), a relevant counterpoint to RAG-based tutors in the wiki.

Methodological Notes

Strengths include a modular, component-level evaluation on authentic student answers and a 30-teacher usability study. Limitations acknowledged by the authors include the lack of student evaluation and a focus on teacher perceptions; deployment priorities are a sub-15-second latency ceiling, adaptive onboarding by user type, and verifier transparency.

Connected Concepts

Connected Articles

Citation

Zhao, R., Bobrov, A., Li, J., Aloisi, C., & He, Y. (2025). LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop. Proceedings of EMNLP 2025. arXiv:2507.04295. DOI: 10.48550/arXiv.2507.04295. (CC BY-NC-ND 4.0)