TY - CONF TI - LLM-based action learning model for construction robotics: an industry-academia co-design C1 - Singapore, Singapore C3 - Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34) SP - 1358 EP - 1369 PY - 2026 DO - 10.24928/2026/0317 AU - Aali, Atefeh AU - Yan, Ruoyu AU - Maalek, Reza AU - Zheng, Xinyu AD - Research Associate/ MSc./ Department of Civil Engineering/ Karlsruhe Institute of Technology (KIT)/ Institute of Technology and Management in Construction, Karlsruhe, Germany, atefeh.aali@kit.edu, orcid.org/0009-0005-7058-9265 AD - Master’s student/ BSc./ Department of Civil Engineering/ Karlsruhe Institute of Technology (KIT)/ Institute of Technology and Management in Construction, Karlsruhe, Germany, ruoyu.yan@student.kit.edu, orcid.org/0009-0009-0784-1850 AD - Senior Lecturer in Digital Engineering in Infrastructure and Built Environment/ Faculty of Engineering and Science/ University of Greenwich, London, United Kingdom, r.maalek@greenwich.ac.uk, orcid.org/0000-0001-6825-2691 AD - Master’s student/ BSc./ Department of Civil Engineering/ Karlsruhe Institute of Technology (KIT)/ Institute of Technology and Management in Construction, Karlsruhe, Germany, xinyu.zheng@student.kit.edu, orcid.org/0009-0009-7962-4839 ED - Hamzeh, Farook ED - Poshdar, Mani ED - Garcia-Lopez,, Nelly P. AB - The construction industry continues to face challenges in adopting advanced technologies, such as construction robotics, from research to practice. From a lean management perspective, this constitutes wasted scientific effort and limited gain in efficiency and productivity of construction processes. Traditionally, interactive workshops that bring together academic and industry practitioners are commonly used to foster knowledge exchange; however, they often lack structured mechanisms to transform diverse expert insights into actionable solutions to address real-world industry challenges. This paper addresses these limitations by introducing a novel AI-assisted workshop method to prioritize critical problems in construction robotics and develop actionable solution pathways. The approach was implemented during a two-day workshop in Germany, which hosted over 100 leaders in construction robotics from academia and industry. An LLM-supported workflow was used to synthesize expert inputs and the data from the literature to identify key areas with potential for further research. These problems were then prioritized through a democratic live polling process involving all participants. Experts were subsequently divided into breakout groups based on predefined criteria and developed solution approaches using their domain knowledge, which were presented in a final plenary session. This paper reports key lessons learned, including outcomes from expert presentations, throughout this process. KW - Lean construction KW - collaboration KW - action learning KW - large language models (LLMs) KW - construction robotics. PB - T2 - Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34) DA - 2026/06/22 CY - Singapore, Singapore L1 - http://iglc.net/Papers/Details/2592/pdf L2 - http://iglc.net/Papers/Details/2592 N1 - Export Date: 19 June 2026 DB - IGLC.net DP - IGLC LA - English ER -