Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models

This paper presents PyFiXV, a system that uses large language models (Codex) to automatically generate feedback for Python syntax errors in introductory programming courses, including both fixed code and natural language explanations. The system incorporates a validation mechanism to ensure high-precision feedback before sharing with students.

Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming. More concretely, given a student's buggy program, our goal is to generate feedback comprising a fixed program along with a natural language explanation describing the errors/fixes, inspired by how a human tutor wou

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