COMPUTER DOMAIN
Published May 6, 2026 | Version v1
Journal article Open

AIVANA: An AI-Powered Intelligent Learning and Automation Platform Using Retrieval-Augmented Generation

Authors/Creators

Description

The increasing volume of digital learning resources has created a need for intelligent systems capable of efficient knowledge retrieval and automation. This paper presents Aivana, an AI-powered intelligent learning platform that integrates Retrieval-Augmented Generation (RAG), semantic search, and full-stack technologies. The system enables users to upload documents, process them using embedding techniques, and retrieve relevant information through FAISS-based vector search. Large language models generate context-aware responses, while additional features such as automated question generation and progress tracking enhance learning outcomes. Experimental results demonstrate improved accuracy, precision, and efficiency compared to traditional methods, making Aivana a scalable solution for modern educational environments.

Files

Files (552.5 kB)

Name Size Download all
md5:1f3aa5bfc225dd0c49b5a0d17e6d07c9
552.5 kB Download

Additional details

References

  • 1. Lewis, P., Perez, E., Piktus, A., et al., 2020, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," NeurIPS, 2020.
  • 2. Karpukhin, V., Oguz, B., Min, S., et al., 2020, "Dense Passage Retrieval for Open-Domain Question Answering," EMNLP, 2020.
  • 3. Izacard, G., & Grave, E., 2021, "Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering," arXiv, 2021.
  • 4. Reimers, N., & Gurevych, I., 2019, "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks," EMNLP, 2019.
  • 5. Touvron, H., Martin, L., Stone, K., et al., 2023, "LLaMA: Open and Efficient Foundation Language Models," arXiv, 2023.
  • 6. OpenAI, 2023, "GPT-4 Technical Report," arXiv, 2023.
  • 7. Brown, T., Mann, B., Ryder, N., et al., 2020, "Language Models are Few-Shot Learners," NeurIPS, 2020.
  • 8. Johnson, J., Douze, M., & Jégou, H., 2021, "Billion-scale Similarity Search with FAISS," IEEE Big Data, 2021.
  • 9. Wolf, T., Debut, L., Sanh, V., et al., 2020, "Transformers: State-of-the-Art Natural Language Processing," EMNLP, 2020.
  • 10. Zhang, Y., Chen, J., & Xu, W., 2024, "Retrieval-Augmented Generation for Academic Knowledge Assistance," ACL, 2024.