AI-Driven Resource Allocation in E-Learning During Internet Fluctuations
Anant Jain, Deepika Pandita · 2024
E-learning platforms are crucial for education worldwide, but they often struggle with fluctuating internet connectivity, which can disrupt the learning experience. This study takes a look at the role of artificial intelligence in optimizing the allocation of resources on these kinds of platforms with a focus on overcoming bandwidth constraints. We suggest a “5A Model” - Assessment, Analysis, Adaptation, Allocation and Automation - which relies on Artificial Intelligence to manage, forecast and automatically change the distribution of resources, allowing platforms to function properly and users are satisfied. To find out the connections' problems and the users' needs, we focused on gathering primary data from teachers and students through FGDs. We provide a comprehensive description of the model's technology including its features and incorporation into the current network of e-learning resources. We conclude that the allocation of resources using the application of AI greatly increases the efficiency of the e-learning systems, creating a good case for the development of e-learning systems in areas where the network is unstable.