Retrieval-Augmented Generation for Multiple-Choice Questions and Answers Generation

Generating a large volume of diverse questions for educational purposes can be a challenging and time-consuming task for educators. Traditional methods like manually writing questions or using templates often fall short when it comes to variety and relevance. In order to cope with these di ffi culties, we have examined a di ff erent approach adopting Retrieval-Augmented Generation ( RAG). RAG is an enhancement technique that allows retrieving independent documents and combines it with artificial intelligence that is able to qualitatively generate contextually adequate questions. In this research, RAG was employed through the Ample LMS platform in which PDF documents were the source of generating MCQ questions. The results of the research indicate that with this method, the process of formulating the questions is relatively faster and the questions generated are of better and diverse in nature. With RAG, the burden of constructing questions is lessened for teachers and the students are provided with better interactive learning opportunities. This approach presents a better alternative that is flexible and suitable for various educational requirements, demonstrating the e ff ectiveness of AI in the improvement of the learning and teaching processes.