# SPHERE: Scaling Personalized Feedback in Programming Classrooms with Structured Review of LLM Outputs

arXiv: https://arxiv.org/abs/2410.16513 · HTML: https://mlsanity.com/paper/2410.16513/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2024.

## Built on by (most cited first, 4 of 4)

- [DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review](https://mlsanity.com/paper/2604.09590/) (2026)
- [Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system](https://mlsanity.com/paper/2603.24940/) (2026)
- [Autograder+: A Multi-Faceted AI Framework for Rich Pedagogical Feedback in Programming Education](https://mlsanity.com/paper/2510.26402/) (2025)
- [An Explainable AI Assistant for Introductory Programming Education: Improving Feedback Reliability with Instructor-AI Collaboration](https://mlsanity.com/paper/2606.12425/) (2026)

## Abstract (arXiv)

Effective personalized feedback is crucial for learning programming. However, providing personalized, real-time feedback in large programming classrooms poses significant challenges for instructors. This paper introduces SPHERE, an interactive system that leverages Large Language Models (LLMs) and structured LLM output review to scale personalized feedback for in-class coding activities. SPHERE employs two key components: an Issue Recommendation Component that identifies critical patterns in students' code and discussion, and a Feedback Review Component that uses a ``strategy-detail-verify'' approach for efficient feedback creation and verification. An in-lab, between-subject study demonstrates SPHERE's effectiveness in improving feedback quality and the overall feedback review process compared to a baseline system using off-the-shelf LLM outputs. This work contributes a novel approach to scaling personalized feedback in programming education, addressing the challenges of real-time response, issue prioritization, and large-scale personalization.