Leveraging LLMs for Dynamic Content Generation and Creating Contextual Quizzes to Enhance Learning Outcomes in Personalized Education

Abstract

The research study developed an adaptive learning system based on LLMs and RAG technology to deliver customized educational content. The system differentiates from traditional LLM educational software by accepting complete lecture materials, which ensure quiz responses and feedback match the specific content of the current course. The application retrieves dynamic, relevant content from lecture slides to provide focused, structured learning that goes beyond standardized, pre-trained responses. Pinecone serves as a vector database for semantic content retrieval, and OpenAI provides GPT for natural language generation from the system architecture. The educational materials undergo Sentence Transformers processing to create semantic embeddings that enable both precise content retrieval as well as contextual adjustments.