Web-Based Automatic Code Evaluation System Using Claude AI for Programming Education - Irawan, Paulus Lucky Tirma; Swastika, Windra (Universitas Dinamika, 2026)
Algorithm and Programming learning face challenges in providing fast and personalized feedback to students. The manual evaluation process conducted by lecturers requires considerable time, hindering students' iterative learning process. This study aims to develop a web submission platform prototype with automatic feedback based on Claude AI to support and enhance the programming learning process. The research method employs a Research and Development (R&D) approach with four stages: needs analysis, system design and planning, platform implementation, and testing and evaluation. The platform was developed using a PHP backend, MySQL database, and Claude AI integration through RESTful web services with a cascading AI evaluation strategy. Evaluation was conducted on 9 students with 39 submissions for three Java assignments with different difficulty levels. Results show the system successfully provides high-quality feedback with an average response time of 2.8 seconds and 100% evaluation success rate. Score distribution shows average improvement from the first assignment (82.3) to the third assignment (87.1), indicating a positive trend in iterative learning. A satisfaction survey of 8 respondents shows the system interface is user-friendly, and AI feedback helps identify syntax and program logic errors. Students made an average of 3.2 attempts per assignment, demonstrating high engagement in the learning process.