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This research examines the incorporation of generative AI technologies in education, emphasizing the creation of an AI-driven educational system that integrates retrieval-augmented generation (RAG), specialized fine-tuned large language models (LLMs), and asynchronous AI agents. The system seeks to improve educational results by providing precise, context-relevant answers customized to the exact requirements of each user, especially in Computer Science and Mathematics. Statistical analysis indicates exceptional accuracy (96.15%) and precision (97.22%) in the Computer Science domain. In Mathematics, the system achieves accuracy (83.34%) and precision (100%). Human assessments underscore clarity, specificity, and precision as primary strengths. Despite obstacles such as broadening domain coverage and maintaining data quality, the results demonstrate the system’s ability to promote adaptable learning environments.

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Leveraging RAG and AI Agents to Enhance Specialized LLMs in Education

  • 会议论文

  • 发表日期：

    2026年

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  • Laila El Jiani,
  • Sanaa El Filali,
  • Mohannad Tazi,
  • Abderrahmane Moujar

摘要

This research examines the incorporation of generative AI technologies in education, emphasizing the creation of an AI-driven educational system that integrates retrieval-augmented generation (RAG), specialized fine-tuned large language models (LLMs), and asynchronous AI agents. The system seeks to improve educational results by providing precise, context-relevant answers customized to the exact requirements of each user, especially in Computer Science and Mathematics. Statistical analysis indicates exceptional accuracy (96.15%) and precision (97.22%) in the Computer Science domain. In Mathematics, the system achieves accuracy (83.34%) and precision (100%). Human assessments underscore clarity, specificity, and precision as primary strengths. Despite obstacles such as broadening domain coverage and maintaining data quality, the results demonstrate the system’s ability to promote adaptable learning environments.