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Enhancing Learning with Retrieval-Augmented Generation in AI Teaching Assistants

Meenu, Shubham Sharma

(11 – 2025)

DOI:

 

This research paper is about creating an AI Teaching Assistant using the Retrieval-Augmented Generation (RAG) method to make learning easier and more effective. In today’s world, artificial intelligence is becoming an important part of education. Many AI tools can help students study, but some of them give limited or outdated answers because they only use the data they were trained on. The RAG approach helps to solve this problem by using two steps. First, it searches for the most suitable information from a large collection of data, and then it uses a language model to generate an answer in simple and clear language. This helps the system give more accurate and useful answers to students’ questions. The AI Teaching Assistant made with this method can help students understand difficult topics, answer their queries, and provide extra study help whenever needed. It can also support teachers by saving time and improving communication with students. Overall, this study shows that RAG can make AI teaching tools more helpful, interactive, and personalized for better learning

 

 

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