CodeGuardians: A Gamified Learning for Enhancing Secure Coding Practices with AI-Driven Feedback - Hadiprakoso, Raden Budiarto; Sihombing, Rudolf Paris Parlindungan (Universitas Multimedia Nusantara, 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