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Retrieval-Augmented Generation (RAG) Based AI Teaching Assistant for Personalized Learning

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Retrieval-Augmented Generation (RAG) combines information retrieval and large language models to provide accurate, context-aware answers. This research develops an AI Teaching Assistant that retrieves educational content from a knowledge base before generating responses. The system improves learning outcomes, reduces hallucinations, and supports personalized education. The architecture integrates document ingestion, embeddings, vector databases, semantic search, and response generation. Retrieval-Augmented Generation (RAG) combines information retrieval and large language models to provide accurate, context-aware answers. This research develops an AI Teaching Assistant that retrieves educational content from a knowledge base before generating responses. The system improves learning outcomes, reduces hallucinations, and supports personalized education. The architecture integrates document ingestion, embeddings, vector databases, semantic search, and response generation. Retrieval-Augmented Generation (RAG) combines information retrieval and large language models to provide accurate, context-aware answers. This research develops an AI Teaching Assistant that retrieves educational content from a knowledge base before generating responses. The system improves learning outcomes, reduces hallucinations, and supports personalized education. The architecture integrates document ingestion, embeddings, vector databases, semantic search, and response generation

Naksh Solutions

Sachin, Abhishek, Ms. Vandana Swami Dr. Rajendra Singh

International Journal of Advanced Research in Science Communication and Technology

Title: Retrieval-Augmented Generation (RAG) Based AI Teaching Assistant for Personalized Learning

Description:

Retrieval-Augmented Generation (RAG) combines information retrieval and large language models to provide accurate, context-aware answers.

This research develops an AI Teaching Assistant that retrieves educational content from a knowledge base before generating responses.

The system improves learning outcomes, reduces hallucinations, and supports personalized education.

The architecture integrates document ingestion, embeddings, vector databases, semantic search, and response generation.

Retrieval-Augmented Generation (RAG) combines information retrieval and large language models to provide accurate, context-aware answers.

This research develops an AI Teaching Assistant that retrieves educational content from a knowledge base before generating responses.

The system improves learning outcomes, reduces hallucinations, and supports personalized education.

The architecture integrates document ingestion, embeddings, vector databases, semantic search, and response generation.

Retrieval-Augmented Generation (RAG) combines information retrieval and large language models to provide accurate, context-aware answers.

This research develops an AI Teaching Assistant that retrieves educational content from a knowledge base before generating responses.

The system improves learning outcomes, reduces hallucinations, and supports personalized education.

The architecture integrates document ingestion, embeddings, vector databases, semantic search, and response generation.

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