CodEzy: An AI-Powered Competitive Coding and Personalized Learning Platform

Abstract: Programming education has evolved significantly with the emergence of online learning platforms and competitive coding environments. However, most existing systems continue to rely on binary evaluation techniques that assess only the correctness of program outputs. Such approaches fail to provide meaningful insights into code quality, algorithmic efficiency, readability, and adherence to software engineering principles. This limitation often prevents learners from understanding their mistakes and improving their programming skills effectively. This paper presents CodEzy, an AI-powered competitive coding and personalized learning platform designed to enhance programming education through adaptive learning, intelligent code evaluation, gamified engagement, and real-time coding competitions. The proposed system integrates personalized tutorials, coding challenges, AI-generated feedback, performance analytics, and skill-based coding duels within a unified ecosystem. Unlike conventional platforms, CodEzy evaluates code beyond correctness by analyzing efficiency, structure, style, and maintainability. The platform employs secure Docker-based sandbox execution, cloud-ready architecture, Redis-powered caching, and external Large Language Models (LLMs) for semantic code analysis and educational content generation. Experimental analysis indicates that the platform can provide detailed AI feedback within acceptable response times while maintaining low- latency interactions for competitive coding environments. The proposed system supports educational institutions, coding bootcamps, and self-learners through adaptive learning, intelligent feedback, and integrated coding practice.

Keywords: Artificial Intelligence, Adaptive Learning, Competitive Programming, Code Evaluation, Gamification, Educational Technology, Learning Analytics, Large Language Models (LLM).