{"id":408,"date":"2026-05-26T20:04:33","date_gmt":"2026-05-27T00:04:33","guid":{"rendered":"https:\/\/dev.www.purdue.edu\/engineering\/I2SAFE-Lab\/?page_id=408"},"modified":"2026-09-17T18:47:21","modified_gmt":"2026-09-17T22:47:21","slug":"generative-ai-for-construction-education","status":"publish","type":"page","link":"https:\/\/www.purdue.edu\/engineering\/I2SAFE-Lab\/generative-ai-for-construction-education\/","title":{"rendered":"Generative AI for Construction Education"},"content":{"rendered":"\n
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Back to Inclusive AI<\/a><\/div>\n<\/div>\n\n\n\n
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Project Overview<\/strong><\/h3>\n\n\n\n
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Rationale<\/strong><\/h3>\n\n\n\n

A Generative AI for Construction class because it demonstrates why general-purpose tools such as ChatGPT may not be sufficient for domain-specific learning and decision-making. The general generative AI systems can produce hallucinated or misleading responses, which weakens accountability and user trust. To address this, this research developed ExpertGen, a domain-tailored generative AI system designed to provide real-time, accurate, and context-specific support. The study compares ExpertGen with ChatGPT and traditional eBooks using learning performance, NASA-TLX cognitive workload, and user trust as evaluation metrics.<\/p>\n\n\n\n

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Impact<\/strong><\/h3>\n\n\n\n
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  • Domain-specific AI matters: <\/strong>Construction AI should be trained or grounded in construction codes, safety rules, BIM, scheduling, and project documents. <\/li>\n\n\n\n
  • Better learning support: ExpertGen helped users perform better than traditional eBook learning and general ChatGPT. <\/li>\n\n\n\n
  • Lower cognitive workload: Domain-tailored AI can help students understand complex construction concepts with less mental effort. <\/li>\n\n\n\n
  • Higher trust: Users trusted ExpertGen more because its answers were more relevant and source-based. <\/li>\n\n\n\n
  • Useful evaluation model: Performance, workload, and trust can be used to evaluate GenAI tools in construction education.<\/li>\n<\/ul>\n\n\n\n
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    Publication List<\/strong><\/h3>\n\n\n\n

    Conference Proceedings<\/strong><\/h4>\n\n\n\n
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    1. Jiang, N., Zhou, W., Hasanzadeh, S., & Duffy Ph D, V. G. (2025). Application of Generative AI in Civil Engineering Education: A Systematic Review of Current Research and Future Directions. In CIB Conferences<\/em> (Vol. 1, No. 1, p. 306).<\/li>\n\n\n\n
    2. Jiang, N., Zhou, W., Hasanzadeh, S., & Duffy, V. G. (2025, May). ExpertGen: A Comparative Analysis of User Performance, Cognitive Workload, and Trust in Domain-Tailored Generative AI. In International Conference on Human-Computer Interaction<\/em> (pp. 178-190). Cham: Springer Nature Switzerland.<\/li>\n<\/ol>\n\n\n\n
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      Researcher<\/strong><\/h3>\n\n\n\n
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      Wei Zhou<\/p>\n<\/div>\n\n\n\n

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      Jae-Hyun Myung<\/p>\n<\/div>\n\n\n\n

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      Collaborators<\/strong><\/h4>\n\n\n\n
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      • Vincent Duffy, PhD, Edwardson School of Industrial Engineering, Purdue University<\/li>\n\n\n\n
      • Nina Jiang, PhD, Department of Mechanical and Industrial Engineering, Louisiana State University<\/li>\n<\/ul>\n\n\n\n
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        Funding Source<\/strong><\/h3>\n\n\n\n
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        <\/p>\n","protected":false},"excerpt":{"rendered":"

        Project Overview Rationale A Generative AI for Construction class because it demonstrates why general-purpose tools such as ChatGPT may not be sufficient for domain-specific learning and decision-making. The general generative AI systems can produce hallucinated or misleading responses, which weakens accountability and user trust. To address this, this research developed ExpertGen, a domain-tailored generative AI […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-408","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.purdue.edu\/engineering\/I2SAFE-Lab\/wp-json\/wp\/v2\/pages\/408","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.purdue.edu\/engineering\/I2SAFE-Lab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.purdue.edu\/engineering\/I2SAFE-Lab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.purdue.edu\/engineering\/I2SAFE-Lab\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.purdue.edu\/engineering\/I2SAFE-Lab\/wp-json\/wp\/v2\/comments?post=408"}],"version-history":[{"count":12,"href":"https:\/\/www.purdue.edu\/engineering\/I2SAFE-Lab\/wp-json\/wp\/v2\/pages\/408\/revisions"}],"predecessor-version":[{"id":920,"href":"https:\/\/www.purdue.edu\/engineering\/I2SAFE-Lab\/wp-json\/wp\/v2\/pages\/408\/revisions\/920"}],"wp:attachment":[{"href":"https:\/\/www.purdue.edu\/engineering\/I2SAFE-Lab\/wp-json\/wp\/v2\/media?parent=408"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}