This project introduces a Multi-Agent System (MAS) designed for adaptive student competency analysis. The system utilizes multiple intelligent agents to dynamically assess and monitor student performance throughout a personalized learning journey. Key agents include Gemini, which generates competency-based questions, SoLPA, which predicts the difficulty of subsequent questions based on student responses, and SoLTA, which adjusts the overall assessment difficulty to ensure an appropriate challenge level. The system tracks the student's progress in real-time, calculates a Competency Index (CI), and provides AI-based feedback, offering personalized insights into strengths and areas for improvement. The adaptive nature of the system allows for a tailored learning experience, enhancing engagement and providing accurate evaluations of student competencies. This approach integrates artificial intelligence, multi-agent systems, and data-driven assessments to foster an effective, personalized educational environment.

Keywords: Multi-Agent System (MAS), Adaptive Learning, Student Competency, Artificial Intelligence (AI), Personalized Assessment, Competency Index (CI), SoLPA, SoLTA, Dynamic Question Generation, Educational Technology, AI-Based Feedback.