CodeGuardians: A Gamified Learning for Enhancing Secure Coding Practices with AI-Driven Feedback - Hadiprakoso, Raden Budiarto; Sihombing, Rudolf Paris Parlindungan (ULTIMA InfoSys, 2024)
This paper introduces CodeGuardians, a gamified platform designed to improve secure coding practices using AI-driven, real-time feedback. The platform focuses on key secure coding concepts, such as input validation, authentication, session management, and cryptography. Developed using the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) instructional model, CodeGuardians enhances engagement and knowledge retention by incorporating interactive challenges. The AI component, powered by OpenAI, provides adaptive feedback on user-submitted code, helping users to learn secure coding practices more effectively. To assess its impact, a one-group pretest-posttest design was conducted. The results of a paired sample t-test showed a significant improvement in secure coding knowledge (t = 19.50, p = 0.048), confirming the platform's effectiveness. In addition, the system's usability was rated highly, with a score of 0.93 on the Computer System Usability Questionnaire (CSUQ), classifying it as "Very Good." The practical implications of this research suggest that CodeGuardians could be implemented in both educational and professional settings to enhance secure coding skills and reduce software vulnerabilities. From a theoretical standpoint, this study advances cybersecurity education by integrating AI-driven feedback into gamified learning environments. The research supports the theory that gamification improves engagement and learning retention, while also highlighting the value of adaptive technologies in addressing real-world security challenges. Future work will examine the long-term retention of knowledge and scalability across diverse learning environments.