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

This paper develops a pedagogical framework for LLM-driven feedback generation in programming education and evaluates it through a mixed-methods study with eight secondary-school computer science teachers using a web-based Python programming application. The study assesses whether LLM-generated feedback aligned with pedagogical principles (mastery adaptation, progress adaptation) can match or exceed human teacher feedback quality.

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 L

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