Design of Adaptive E-Learning Applications: Overcoming Limitations Through Modular and Scalable Architectures

Abstract

Adaptive Learning Systems (ALS) represent an effective approach for implementing adaptation processes in e-learning platforms, enabling the personalization of the educational experience based on the learner’s characteristics, performance, and context. However, many current implementations face design, technological, and operational constraints that compromise key ALS properties, such as personalized content delivery, continuous adaptive assessment, immediate and contextualized feedback, technical modularity and scalability, adaptive collaboration through intelligent grouping, and ethical and secure information management. These properties are conceptually characterized in this work to support the analysis of the underlying technological challenges. This article presents a representative diagnosis of these constraints, aiming to highlight recurrent challenges in the design and implementation of adaptive environments. Based on this diagnosis, key technological requirements are defined, and an architectural solution is proposed, grounded in principles of modularity, separation of concerns, and event-driven design. The proposal materializes in a distributed infrastructure supported by a microservices-based architecture that enables the integration of specialized components for adaptive functionalities. This approach provides a suitable technical and design alternative for the development and evolution of personalized e-learning environments in the ALS paradigm.