Generative AI-Driven Digital Assistance for E-Learning: A Novel Paradigm for Personalized Recommendations

With the continuous proliferation of E-Learning platforms, the demand for intelligent and adaptive systems to guide learners through vast content repositories has grown. Drawing inspiration from recent advancements in recommendation systems, particularly within digital library contexts, this paper presents an innovative approach using Generative AI to define digital assistance in E-Learning environments. Unlike traditional recommendation systems, which suggest resources based on explicit patterns or user metadata, our Generative AI model dynamically crafts personalized content based on learners' preferences and progress. We juxtapose our methods with prevalent deep learning-based recommendation systems as discussed in prior research. The novelty of our approach lies in the amalgamation of generative algorithms with personalized recommendation, thereby offering a dynamic, real-time, and context-aware learning guide. The paper elaborates on the model's architecture, its performance metrics in comparison to existing methods, and its implications for the future of digital education. Through this study, we hope to pave the way for more intuitive, adaptive, and responsive E-Learning experiences.