Intelligent Adaptive Learning Ecosystem: A Multi- Agent AI Framework for Personalized Education

Modern educational tools face significant challenges in delivering innovative and diverse information to students across various fields. This paper presents the Intelligent Adaptive Learning Ecosystem (IALE) framework, which uses Large Language Model (LLM) for automated, personalized course generation. The system employs the Groq API with the Llama-3.3-70b versatile model to dynamically create customized curricula based on userdefined parameters. These parameters include learning style (mix, theoretical, project-based), time availability (1 hour/day, 3 hours/day, weekends), skill level (beginner, intermediate, advanced), and specific module needs. The implementation uses Next.js for the responsive frontend interface and FastAPI for backend services, resulting in a scalable course generation platform. Preliminary evaluation shows that the system can create contextually appropriate, structured learning paths with high user satisfaction. Future improvements will focus on generating career roadmaps, offering blockchain-based certification, and adding an interactive project builder chatbot. The framework fills critical gaps in current adaptive systems by removing the need for manual course design while ensuring quality through LLMpowered content structuring.