TY  - CONF
TI  - Generative AI in Lean Construction: a Scoping Review
C1  - Osaka and Kyoto, Japan
C3  - Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)
SP  - 953
EP  - 964
PY  - 2025
DO  - 10.24928/2025/0195
AU  - Rad, Mohammad Hamed Ganji
AU  - Ilbeigi, Mohammad
AD  - Ph.D. Student, Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology, Hoboken, New Jersey, USA, mganjira@stevens.edu, orcid.org/0009-0005-4502-1053
AD  - Assistant Professor, Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology, Hoboken, New Jersey, USA, milbeigi@stevens.edu, orcid.org/0000-0001-6576-3808
ED  - Seppänen, Olli 
ED  - Koskela, Lauri 
ED  - Murata , Koichi 
AB  - Generative Artificial Intelligence (AI), specifically Large Language Models (LLMs), has the potential to reshape construction management practices. These novel solutions can transform lean construction by enabling real-time data analysis, streamlined communication, and automated decision-making across project teams. They can facilitate enhanced collaboration by generating insights from vast construction data sets, improving workflow efficiency, and reducing waste. Additionally, LLMs can support predictive modeling, proactive risk management, and knowledge sharing, aligning with lean principles of maximizing value and minimizing inefficiencies. Given the recent advancements in generative AI, it is critical to systematically shape future research directions by building on the existing body of knowledge and addressing key knowledge gaps. The first step toward identifying knowledge gaps and uncovering critical areas that remain underexplored is to systematically analyze the existing body of knowledge. Therefore, this study conducts a scoping review to synthesize the extent, range, and nature of existing studies that have proposed novel solutions using generative AI and LLMs for various aspects of construction management. The outcomes of this systematic scoping review will help identify potential research directions for future studies in this domain.
KW  - Generative AI
KW  - LLMs
KW  - Construction
KW  - Scoping Review.
PB  - 
T2  - Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)
DA  - 2025/06/02
CY  - Osaka and Kyoto, Japan
L1  - http://iglc.net/Papers/Details/2368/pdf
L2  - http://iglc.net/Papers/Details/2368
N1  - Export Date: 01 June 2025
DB  - IGLC.net
DP  - IGLC
LA  - English
ER  -