International conference on construction engineering and project management (국제학술발표논문집)

  • 2024.07a
  • /
  • Pages.1230-1237
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  • 2024
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  • 2508-9048(eISSN)
Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Multi-agent Conversational AI System for Personalized Learning of Construction Knowledge.

  • Rahat HUSSAIN (School of Architecture and Building Science, Chung-Ang University) ;
  • Aqsa SABIR (Department of Computer Science and Engineering, Chung-Ang University) ;
  • Muahmmad Sibtain ABBAS (School of Architecture and Building Science, Chung-Ang University) ;
  • Nasrullah KHAN (School of Architecture and Building Science, Chung-Ang University) ;
  • Syed Farhan Alam ZAIDI (School of Architecture and Building Science, Chung-Ang University) ;
  • Chansik PARK (School of Architecture and Building Science, Chung-Ang University) ;
  • Doyeop LEE (School of Architecture and Building Science, Chung-Ang University)
  • Published : 2024.07.29
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Abstract

Personalized learning is a critical factor in optimizing performance on construction sites. Traditional pedagogical methods often adhere to a one-size-fits-all approach, failing to provide the nuanced adaptation required to cater to diverse knowledge needs, roles, and learning preferences. While advancements in technology have led to improvements in personalized learning within construction education, the crucial connection between instructors' roles and training enviornment to personalized learning success remains largely unexplored. To address these gaps, this research proposes a novel learning approach utilizing multi-agent, context-specific AI agents within construction virtual environments. This study aims to pioneer an innovative approach leveraging the Large Language Model's capabilities with prompt engineering to make domain-specific conversations. Through the integration of AI-driven conversations in a realistic 3D environment, users will interact with domain-specific agents, receiving personalized safety guidance and information. The system's performance is assessed using the five evaluation criteria including learnability, interaction, communication, relevancy and visualization. The results revealed that the proposed approach has the potential to significantly enhance safety learning in the construction industry, which may lead to improve practices and reduction in accidents on diverse construction sites.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A2B5B02002553). This research was conducted with the support of the "National R&D Project for Smart Construction Technology (No.RS-2020-KA156291)" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.

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