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Abstract <p>An adaptive automated response system for Virtual Computer Lab and LMS Moodle is presented, leveraging Retrieval-Augmented Generation (RAG), a fine-tuned Llama (or Gemma, Qwen, etc.) model, and serverless architecture. Integrated with Moodle and Supabase, it delivers context-aware responses tailored to user roles (student, instructor, administrator). A self-learning mechanism driven by feedback enhances response accuracy and reducing technical support workload. An interactive interface with custom widgets improves user experience.</p>

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Adaptive Automated Response System for Virtual Computer Lab and LMS Moodle Using LLM, RAG, and Serverless Architecture

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Yu. N. Skulskiy,
  • E. N. Cheremisina,
  • E. F. Kirov,
  • E. Yu. Kirpicheva,
  • E. M. Mazhitova,
  • Yu. Yu. Medvedeva,
  • A. V. Nechaevsky,
  • S. V. Potemkina,
  • D. S. Sidorov,
  • N. A. Tokareva

摘要

Abstract

An adaptive automated response system for Virtual Computer Lab and LMS Moodle is presented, leveraging Retrieval-Augmented Generation (RAG), a fine-tuned Llama (or Gemma, Qwen, etc.) model, and serverless architecture. Integrated with Moodle and Supabase, it delivers context-aware responses tailored to user roles (student, instructor, administrator). A self-learning mechanism driven by feedback enhances response accuracy and reducing technical support workload. An interactive interface with custom widgets improves user experience.