Adaptive Learning Path Recommendation Engine

Design a machine learning-powered recommendation system that dynamically generates personalized learning paths for students based on their academic performance, learning style, and skill gaps. The system should utilize collaborative filtering and neural network algorithms to predict optimal curriculum progression. Create a modular architecture that can integrate with existing learning management systems, with specific consideration for handling diverse data sources like assessment scores, interaction logs, and student metadata.