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CB Ps I5das22114 Report by gopim302004 is a document available to read on EtoBox.

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This dissertation presents a Personalized Learning Assistant utilizing a Memory-Augmented Retrieval-Augmented Generation (RAG) framework to enhance educational applications of Large Language Models (LLMs). The system integrates a persistent memory store to track learner interactions, enabling personalized and context-aware responses based on user-uploaded study materials. Evaluation results demonstrate significant improvements in response quality and engagement metrics compared to traditional retrieval-disa

Author

gopim302004

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EN