# Partnering with AI: A Pedagogical Feedback System for LLM Integration into Programming Education

arXiv: https://arxiv.org/abs/2507.00406 · HTML: https://mlsanity.com/paper/2507.00406/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2025.

## Built on by (most cited first, 5 of 5)

- [Enhancing Diversity of LLM-Generated Educational Tasks](https://mlsanity.com/paper/2512.23601/) (2025)
- [When Verification Hurts: Asymmetric Effects of Multi-Agent Feedback in Logic Proof Tutoring](https://mlsanity.com/paper/2603.27076/) (2026)
- [Exploring Teacher-Chatbot Interaction and Affect in Block-Based Programming](https://mlsanity.com/paper/2603.20211/) (2026)
- [REFINE: Real-world Exploration of Interactive Feedback and Student Behaviour](https://mlsanity.com/paper/2603.29142/) (2026)
- [Autograder+: A Multi-Faceted AI Framework for Rich Pedagogical Feedback in Programming Education](https://mlsanity.com/paper/2510.26402/) (2025)

## Abstract (arXiv)

Feedback is one of the most crucial components to facilitate effective learning. With the rise of large language models (LLMs) in recent years, research in programming education has increasingly focused on automated feedback generation to help teachers provide timely support to every student. However, prior studies often overlook key pedagogical principles, such as mastery and progress adaptation, that shape effective feedback strategies. This paper introduces a novel pedagogical framework for LLM-driven feedback generation derived from established feedback models and local insights from secondary school teachers. To evaluate this framework, we implemented a web-based application for Python programming with LLM-based feedback that follows the framework and conducted a mixed-method evaluation with eight secondary-school computer science teachers. Our findings suggest that teachers consider that, when aligned with the framework, LLMs can effectively support students and even outperform human teachers in certain scenarios through instant and precise feedback. However, we also found several limitations, such as its inability to adapt feedback to dynamic classroom contexts. Such a limitation highlights the need to complement LLM-generated feedback with human expertise to ensure effective student learning. This work demonstrates an effective way to use LLMs for feedback while adhering to pedagogical standards and highlights important considerations for future systems.