A Comprehensive Survey of an Agentic AI Powered Multimodal RAG Learning System

Abstract: Modern-day education has changed due to the utilization of artificial intelligence by implementing adaptive learning systems tailored towards individual students. Traditional learning methods generally present the same content to every learner without considering individual learning speed, understanding, or preferences. To address this limitation, this project proposes Data Spark Assistant – An Agentic AI Powered Multimodal RAG Learning System, an intelligent learning platform that delivers personalised educational support using Artificial Intelligence (AI). The system combines Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), and Agentic AI to analyse learner performance and generate customised study materials. Through intelligent quizzes and assessments, the platform identifies each learner's strengths, weaknesses, and knowledge gaps. Based on this analysis, it recommends personalised lessons, practice questions, and learning resources that help students improve in weaker areas. By integrating adaptive learning techniques with AI-driven content generation, the proposed system creates an interactive and personalised learning environment. This approach enhances learning efficiency, encourages self-paced study, promotes critical thinking, and supports better academic performance.

Keywords: Adaptive Learning, Agentic AI, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Natural Language Processing (NLP), Knowledge Gap Analysis, User Profiling, Multimodal Learning.