AI-Driven Code Analysis and Feedback System for Educational Environments Using Offline Language Models

Abstract

The evolution of computer science education has created an urgent need for intelligent systems that can evaluate and guide student programming practices efficiently. Traditional manual evaluation methods are often subjective, inconsistent, and time-consuming, leading to reduced feedback quality and limited student engagement. This paper presents an AI-Driven Code Analysis and Feedback System that employs offline language models to automatically analyze and provide personalized feedback on student code in educational environments. Unlike cloud-based models, offline deployment ensures data privacy, low latency, and uninterrupted access in bandwidth-constrained institutions. The system leverages static and semantic code analysis supported by lightweight neural networks that detect syntax and logical errors, assess code quality, and generate human-readable improvement suggestions. The research demonstrates that offline AI-based code evaluation can achieve accuracy comparable to cloud solutions while maintaining educational autonomy and ethical data handling.