International Journal of Science, Engineering and TechnologyAuthors: Rivan Chavan
Abstract: The rapid transition from traditional pedagogical models to digital-first environments has exposed a fundamental conflict between the need for global scalability and the demand for personalized instruction. This review article investigates the synergy between scalable cloud computing architectures and artificial intelligence (AI) techniques as the primary solution to this challenge. We analyze how cloud service models (SaaS, PaaS, and IaaS) provide the elastic infrastructure necessary to support massive concurrent user bases, while AI methodologies specifically machine learning for adaptive learning paths and natural language processing for intelligent tutoring transform static content into dynamic, learner-centric experiences. The article further explores the technical convergence at the cloud edge, where decentralized processing enables low-latency immersive learning through AR/VR. Critical attention is given to the ethical dimensions of this integration, including data privacy compliance (GDPR/FERPA), the mitigation of algorithmic bias, and the necessity for explainable AI in academic assessment. By synthesizing current research and industrial case studies, this review provides a strategic roadmap for the development of "intelligence-native" e-learning platforms, forecasting a shift toward autonomous, lifelong learning ecosystems by 2030.
DOI: https://doi.org/10.5281/zenodo.18221378
