A Containerized Microservices Architecture with Reinforcement Learning for Scalable, Adaptive Learning

This paper presents the design and validation of a scalable, containerized web platform for real-time adaptive language instruction, integrating reinforcement learning into a modern microservices architecture. The system leverages Docker and Kubernetes for container orchestration, implements RESTful APIs and WebSocket communication for low-latency interaction, and utilizes Kafka for event-driven messaging between independently deployable services. Technical performance and operational health are monitored through a unified ELK stack dashboard, providing real-time insights for both developers and instructors. Empirical evaluation under authentic production conditions demonstrates the platform’s ability to sustain high availability (99.95% uptime), rapid response times (frontend < 120 ms, backend ≈180 ms), and robust autoscaling for workloads exceeding 150 concurrent users. Rigorous stress testing confirms seamless failover and rapid recovery, while the analytics layer enables actionable monitoring of both infrastructure and learner engagement. A field study with 154 language learners further validates the platform’s effectiveness, as reinforcement-learning-driven personalization resulted in a 24.7 percentage point gain in post-test performance and improved retention compared to rule-based controls. This work offers a transferable blueprint for web-native, AI-powered learning environments and demonstrates how service-oriented engineering can bridge the gap between pedagogical innovation and large-scale, real-world deployment.