A Scalable Microservices-Based Architecture for E-learning Platforms

Abstract

E-learning platforms have become critical infrastructure for knowledge dissemination, flexible scheduling, and lifelong upskilling. Their relevance was underscored during the COVID-19 pandemic, which exposed recurring gaps in widely used systems: brittle scalability under surges, uneven customization across institutional contexts, fragmented support workflows, and limited, siloed analytics. These constraints hindered timely interventions, raised operational costs, and complicated governance and maintenance as deployments grew and heterogeneity. This paper proposes a scalable architecture for e-learning that combines a microservices-based LMS with an integrated knowledge hub and gamification. The design supports institutional customization, observability, and elastic scaling. A comparative analysis with six leading platforms highlights the architectural advantages and addresses scalability and support gaps exposed during the pandemic. To evaluate the relevance and advantages of this architecture, an integrated Artificial Intelligence layer provides conversational support and data-driven recommendations grounded in usage and performance signals. We complement the architectural description with a comparative analysis against six established platforms, using a set of evaluation variables defined in the methodology (architecture, licensing, implementation type, scalability, customization, ecosystem, integration/extensibility, and gamification), showing how the proposed approach addresses pandemic-revealed gaps while delivering SaaS-like elasticity without forfeiting institutional control.