A Digital Recommendation System for Personalized Learning to Enhance Online Education A Review.

This review explores the application of e-learning platforms and tailored recommendation schemes in the educational sector. It analyzes 60 research articles sourced from leading hypothetical databases, highlighting the various techniques employed in sanction structures, comprising cooperative riddling, relaxed-constructed straining, and the emerging use of machine learning models. Despite advancements, existing personalized learning systems still encounter challenges such as poor content comprehension, student dropouts, language limitations, difficulties in choosing appropriate study resources, and limitations in infrastructure and funding. To address these problems, the study suggests integrating innovative digital tools like Fluxy AI, Twin technology, AI-driven virtual proctoring systems, and the Alter Ego interface. These tools have the potential to foster an engaging and adapt erudition environment by offering modified enlightening involvements for pupils and actionable insights for educators. Their implementation may significantly enhance personalized learning, boost students’ comprehension levels, and support learners with speech-related challenges more effectively