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In the construction industry, the advent of teleoperation and robotic technologies is revolutionizing traditional recruitment prac-tices, introducing new criteria for identifying qualified workers. This evolution presents significant challenges for employers aiming to recruitworkers who can maximize organizational utility. Although contract theory offers a promising solution to these challenges, its inherent self-disclosure property could inadvertently lead to privacy breaches, such as revealing gender-related information. Such disclosure risk mightintensify existing biases, notably gender bias, within the sector. To this end, we proposed deep reinforcement learning (DRL)-based contracttheory. Firstly, the trained DRL model will produce unpredictable contract bundles, restricting employers’ access to workers’ privacy. Sub-sequently, to ensure employers adopt DRL-based contract theory, we utilized blockchain to supervise contract bundle generation. Finally,given that the DRL models are homogenous among employers, we integrated transfer learning to reduce unnecessary overhead. Simulationexperiments conducted using US labor force statistical data demonstrated that our work can effectively mitigate potential gender bias byaugmenting the contract selection rights for female workers from 72.73% and 60% to 96.97% and 95% in comparison with traditionalcontract theory while maximizing employers’ utility. In addition, with the integration of transfer learning, the training overhead ofDRL-based contract theory can decrease by 50%. The meaning and significance of the results lie in the innovative integration of contracttheory, deep reinforcement learning, and transfer learning into the recruitment framework, significantly advancing the body of knowledge inunbiased workforce development. DOI: 10.1061/JCEMD4.COENG-15330. © 2024 American Society of Civil Engineers. 

In the construction industry, building information modeling (BIM) has been widely utilized in design coordination. However, this process is time-consuming to query the required element information and still requires the support of the BIM coordinator. Meanwhile, during diverse participants’ discussions, it is challenging to record knowledge and experiences residing in their minds and timely respond to them in the BIM model. GPT-based Large Language Models (LLMs) enable providing automatic solutions but lack accuracy and consistency, specifically for the construction domain. To bridge these gaps, we propose to develop an AI BIM coordinator by integrating basic construction knowledge and skillset into the current AutoGen model. It aims to alleviate high-skill requirements and specific functions of traditional BIM development while enhancing the interdisciplinary interpretability and performance of AI models. Specifically, we first identify the frequent and common interactions between BIM and project teams during the design coordination meetings. Correspondingly, we build the skillset that includes basic functions regarding building element semantic, geometric, and topological information. With this skillset, our designed workflow can interpret 3D BIM space and answer specific questions from users through flexible revisions and extensions. Beyond the text responses that describe relations among elements, the BIM tool can be automatically invoked to execute this task and the model can be directly built in the 3D environment for stakeholders’ discussions in the design coordination meetings. If failed, our designed checker agent will regenerate the code until execution is succeeded. As users continually communicate with the AI BIM coordinator and provide feedback, the assistant can collect and annotate these data for fine-tuning the current model to make it more adaptive to specific construction tasks. For validation, a prototype system is developed with building design coordination meeting data. The results demonstrate that our designed workflow has better performance in execution succeeded rate (84.62%) and accuracy (76.92%) despite consuming more time (1 min 12 secs – 3 mins 1 sec) than general agent workflow.