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CODEVERSITY

Author: Dr. K. P. Malarkodi and Madhan S

Published On: 2026-03-06

Abstract

The rapid evolution of digital technologies has significantly transformed educational methodologies, particularly in the field of computer science and programming education. Traditional classroom - based coding instruction often faces limitations such as delayed feedback, lack of personalization, insufficient engagement, and difficulty in tracking individual student performance at scale. To address these challenges, this paper presents CodeVersity, an AI-powered coding education platform designed to provide an intelligent, interactive, and scalable learning environment for students and educators.

CodeVersity integrates structured learning modules, real-time code execution, AI-driven evaluation using Google Genkit AI, gamification elements, collaborative features, and instructor analytics into a unified web-based platform. Students can learn programming languages such as Python, Java, C, and C++ through guided tutorials and interactive coding exercises. The platform automatically evaluates submitted code, generates scores, provides detailed feedback, and offers contextual hints to promote independent problem-solving and mastery.

 

The system is developed using modern web technologies including Next.js, React, Tailwind CSS, Firebase Authentication, and Firestore to ensure scalability, security, and real-time synchronization. Teachers are equipped with a dashboard for managing assignments, tracking student progress, organizing groups, and generating analytics reports.

 

This research paper presents the system architecture, design methodology, AI evaluation framework, gamification model, implementation details, experimental evaluation, performance analysis, benefits, limitations, and future enhancements. The findings demonstrate that integrating AI-driven feedback and gamification significantly enhances student engagement, coding proficiency, and self-paced learning efficiency in institutional environmen

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Article Information
Volume

9

Year

2026

Review Rounds

1

Article Type

Research Article

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