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Generative AI (GenAI) shows potential for personalising learning experiences, promoting new skill, and acquiring new 4.0 technologies in Architecture, Engineering and Construction (AEC). Building Information Modelling (BIM) is one of the most sought-after and proven technologies. This paper provides a comprehensive and bibliometric analysis of how GenAI is used in BIM education. By analysing 14 documents from different databases, it is found that Architecture and Engineering programmes are benefiting from the adoption of GenAI via Large Language Models (LLMs), Generative Adversarial Networks (GANs) and Natural Language Processing (NLP) have been adopted to convey mainly skills in conceptual design (BIM 3D), construction management (BIM Management) and sustainability techniques (BIM 6D). Future studies should broaden and build on this initial analysis, providing a foundation for exploring more GenAI tools and experimenting with more aspects of BIM in a variety of academic settings. This article triggers and highlights the need to experiment with GenAI more transparently and securely to ensure reliable and scientific learning of BIM skills.

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Innovative Approaches for BIM Education Through Generative AI

  • 会议论文

  • 发表日期：

    2026年

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  • Khalil Idrissi Gartoumi,
  • Stéphane Cédric Tékouabou Koumetio,
  • Youssef El Ganadi,
  • Mariame Chahbi

摘要

Generative AI (GenAI) shows potential for personalising learning experiences, promoting new skill, and acquiring new 4.0 technologies in Architecture, Engineering and Construction (AEC). Building Information Modelling (BIM) is one of the most sought-after and proven technologies. This paper provides a comprehensive and bibliometric analysis of how GenAI is used in BIM education. By analysing 14 documents from different databases, it is found that Architecture and Engineering programmes are benefiting from the adoption of GenAI via Large Language Models (LLMs), Generative Adversarial Networks (GANs) and Natural Language Processing (NLP) have been adopted to convey mainly skills in conceptual design (BIM 3D), construction management (BIM Management) and sustainability techniques (BIM 6D). Future studies should broaden and build on this initial analysis, providing a foundation for exploring more GenAI tools and experimenting with more aspects of BIM in a variety of academic settings. This article triggers and highlights the need to experiment with GenAI more transparently and securely to ensure reliable and scientific learning of BIM skills.