Nowadays, it can be seen that Artificial Intelligence (AI) has become increasingly present in the field of education, assuring new possibilities for personalizing learning, analyzing student performance, and improving educational support. However, the effective implementation of AI algorithms in educational platforms is often limited by available hardware resources and difficulties related to scalability, interoperability, and maintenance. This paper proposes a containerized architectural framework for integrating machine learning templates into educational environments, with the aim of improving the accessibility, modularity, and portability of AI-based solutions. The architecture uses containerization technologies such as Docker and Kubernetes to manage AI components in isolation, including supervised, unsupervised, and reinforcement learning algorithms. Through this approach, educational units can benefit from a flexible, scalable and reproducible infrastructure, which allows the development of adaptable and efficient solutions to support the educational process within various contexts, including distance or hybrid learning. Both teachers and students will benefit because this type of architecture will increase the efficiency of online learning platforms or platforms for practicing knowledge acquired at school or in courses. The paper provides a theoretical perspective on the benefits of AI containerization in education, highlighting the potential of these solutions to facilitate the rapid integration of emerging technologies. Also practical application scenarios and future research directions regarding containerized infrastructures for artificial intelligence in education are discussed, including integration with LMS (Learning Management System) platforms and learning analytics systems.

Keywords: Education, educational platforms, adaptive digital education, artificial intelligence, Docker, architecture, Kubernetes.