This paper provides an in-depth analysis of the outcomes of 'AI for Construction Project Management' summer school, a pioneering program that received the 2024 CIB Vistas Funding for its role in advancing Artificial Intelligence (AI) applications in construction project management. The summer school comprised six sessions that introduced a range of AI techniques—including Machine Learning (ML), Probabilistic Graphical Models, Computer Vision, And Digital Twins—each designed to automate and optimize key construction tasks. The program engaged 162 participants from industry and academia, representing diverse educational and professional backgrounds. During the introductory sessions and hand-on exercises, the participants explored real-world challenges such as project scheduling, cost estimation, safety management, and resource allocation. At the conclusion of the summer school, all participants were invited to complete a detailed survey assessing the educational content, logistics of each session, and the overall organization of the program. Their feedback emphasized the practical value and relevance of the AI techniques presented, highlighting the potential for further research and development. This paper includes a comprehensive review of each session, a sentiment analysis of participant feedback, correlations between participants' backgrounds and the topics they found most compelling, and an evaluation of AI's transformative potential in construction management. These findings contribute significantly to aligning AI advancements with industry and research needs and provide valuable insights for designing impactful educational programs for professionals and researchers in construction. It also emphasizes the necessity to develop specialized and practical educational content on AI to increase the efficiency and success rate of projects.

AI in Construction Project Management education: Insights from the 'AI for Construction Project Management' Summer School

Khodabakhshian, Ania;Re Cecconi, Fulvio
2025-01-01

Abstract

This paper provides an in-depth analysis of the outcomes of 'AI for Construction Project Management' summer school, a pioneering program that received the 2024 CIB Vistas Funding for its role in advancing Artificial Intelligence (AI) applications in construction project management. The summer school comprised six sessions that introduced a range of AI techniques—including Machine Learning (ML), Probabilistic Graphical Models, Computer Vision, And Digital Twins—each designed to automate and optimize key construction tasks. The program engaged 162 participants from industry and academia, representing diverse educational and professional backgrounds. During the introductory sessions and hand-on exercises, the participants explored real-world challenges such as project scheduling, cost estimation, safety management, and resource allocation. At the conclusion of the summer school, all participants were invited to complete a detailed survey assessing the educational content, logistics of each session, and the overall organization of the program. Their feedback emphasized the practical value and relevance of the AI techniques presented, highlighting the potential for further research and development. This paper includes a comprehensive review of each session, a sentiment analysis of participant feedback, correlations between participants' backgrounds and the topics they found most compelling, and an evaluation of AI's transformative potential in construction management. These findings contribute significantly to aligning AI advancements with industry and research needs and provide valuable insights for designing impactful educational programs for professionals and researchers in construction. It also emphasizes the necessity to develop specialized and practical educational content on AI to increase the efficiency and success rate of projects.
2025
CIB Conferences
Artificial intelligence, Education, Project Management.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1296584
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