Recent advancements in artificial intelligence (AI), particularly in natural language processing (NLP), have led to the development of sophisticated chatbots like ChatGPT. These chatbots excel in answering questions and can significantly improve the learning experience by providing immediate feedback and support to students. They enable continuous learning outside traditional classrooms by understanding and responding to questions with human-like proficiency. However, their responses are limited to the information they were trained on. In this study, we employ the Retrieval-Augmented Generation technique to enable these chatbots to access and retrieve relevant information from external knowledge sources (such as course material) to improve the generation capabilities of these models as virtual teaching assistants.