Personalized Learning Paths with AI-Powered Content Recommendation

In today's rapidly evolving educational landscape, personalization in learning is key to maximizing student engagement and success. Our project seeks to harness AI and machine learning technologies to create a sophisticated platform that delivers personalized learning paths to students. By leveraging technologies such as OpenAI API, TensorFlow, and Hugging Face, we aim to develop a system that uses large language models and natural language processing to analyze student interactions and preferences in real-time. This system will generate recommendations for study materials, videos, quizzes, and other educational resources tailored to individual learning styles. The platform will integrate seamlessly with popular learning management systems, offering a user-friendly interface for educators and students alike. By employing predictive analytics, it will identify potential learning challenges and suggest interventions to keep students on track. The project will also explore utilizing computer vision and YOLO for content analysis and AutoML for optimizing models without extensive manual intervention. This solution addresses the growing demand for adaptive learning technologies in hybrid and remote learning environments, promising to enhance educational outcomes and student satisfaction significantly.