Leveraging GPT-4 for Accuracy in Education: A Comparative Study on Retrieval-Augmented Generation in MOOCs

Large Language Models (LLMs), such as Generative Pretrained Transformers (GPTs), have demonstrated remarkable capabilities in natural language processing (NLP). However, these models often encounter challenges such as inaccuracies and hallucinations, which can undermine their utility. Retrieval-Augmented Generation (RAG) has emerged as a promising approach to enhance model accuracy and reliability by integrating external databases. This study investigates the use of RAG to improve the accuracy of GPT models in educational settings, particularly within the realm of Massive Open Online Courses (MOOCs). Through a comparative analysis of various GPT model iterations, we observed a significant improvement in accuracy, increasing from 60% with GPT-3.5 to 80% using the RAG-augmented GPT-4. This enhancement highlights the considerable potential of RAG-augmented GPT models in improving the accuracy of content generation. Such enhanced accuracy suggests revolutionizing assessment methodologies and learning experiences, fostering an educational environment that is more interactive and tailored to individual needs.