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In academic, combining explicit knowledge from documents and implicit knowledge from interviews is a major challenge. This research introduces an automated Knowledge Acquisition System (KAS) utilizing Large Language Models (LLMs) to enhance knowledge extraction. The system addresses inefficiencies in capturing explicit information from academic sources while incorporating expert insights from interviews. The results show significant improvements in key academic processes such as course registration, final projects, and graduation. This research offers a holistic, efficient approach to managing academic knowledge, providing solutions to common administrative challenges.

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Automatic Knowledge Acquisition System with Large Language Model in Academic Domain

  • 会议论文

  • 发表日期：

    2025年

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  • Ahmad Julius Tarigan,
  • Kemas Rahmat Saleh Wiharja,
  • Dade Nurjanah

摘要

In academic, combining explicit knowledge from documents and implicit knowledge from interviews is a major challenge. This research introduces an automated Knowledge Acquisition System (KAS) utilizing Large Language Models (LLMs) to enhance knowledge extraction. The system addresses inefficiencies in capturing explicit information from academic sources while incorporating expert insights from interviews. The results show significant improvements in key academic processes such as course registration, final projects, and graduation. This research offers a holistic, efficient approach to managing academic knowledge, providing solutions to common administrative challenges.