AI Code Debugger for Beginners: An Explainable Error Interpretation System for Programming Education

This paper presents the design and prototype implementation of an AI-assisted debugging system aimed at supporting beginner Python programmers. Traditional debugging tools often display raw error messages that are difficult for novices to interpret, creating barriers in the learning process. To address this, the proposed system focuses on explainable error interpretation by converting technical Python errors into structured, beginner-friendly explanations. The system identifies error types, analyzes code context, and generates simplified feedback that helps learners understand the cause of errors, how to fix them, and how to avoid similar mistakes in the future. The project follows an iterative development approach, including system design, rule-based classification, and explanation generation. The objective is to improve programming comprehension and reduce dependency on external debugging assistance among beginners. Prototype evaluation indicates that structured explanations improve clarity and understanding compared to traditional error messages.