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This study explores the use of Large Language Models (LLMs), specifically GPT, for different safety management applications in the construction industry. Many studies have explored the integration of GPT in construction safety for various applications; their primary focus has been on the feasibility of such integration, often using GPT models for specific applications rather than a thorough evaluation of GPT’s limitations and capabilities. In contrast, this study aims to provide a comprehensive assessment of GPT’s performance based on established key criteria. Using structured use cases, this study explores GPT’s strength and weaknesses in four construction safety areas: (1) delivering personalized safety training and educational content tailored to individual learner needs; (2) automatically analyzing post-accident reports to identify root causes and suggest preventive measures; (3) generating customized safety guidelines and checklists to support site compliance; and (4) providing real-time assistance for managing daily safety tasks and decision-making on construction sites. LLMs and NLP have already been employed in each of these four areas for improvement, making them suitable areas for further investigation. GPT demonstrated acceptable performance in delivering evidence-based, regulation-aligned responses, making it valuable for scaling personalized training, automating accident analyses, and developing safety protocols. Additionally, it provided real-time safety support through interactive dialogues. However, the model showed limitations in deeper critical analysis, extrapolating information, and adapting to dynamic environments. The study concludes that while GPT holds significant promise for enhancing construction safety, further refinement is necessary. This includes fine-tuning for more relevant safety-specific outcomes, integrating real-time data for contextual awareness, and developing a nuanced understanding of safety risks. These improvements, coupled with human oversight, could make GPT a robust tool for safety management. 

The rapid advancements in Artificial Intelligence (AI) hold the promise of transformative benefits across industries, including construction. To navigate this changing landscape, construction students must not only harness AI's potential but also grasp its ethical considerations and potential challenges. As such, there is a growing imperative within construction education to foster AI literacy among prospective professionals. This study developed and integrated an AI in Construction course module into an undergraduate construction management course. The primary goal is to equip students with AI literacy, achieved through a comprehensive approach that encompasses both theoretical knowledge, covering essential AI concepts and their applications in construction, and practical hands-on experiences, exemplified by a project focused on computer vision for personal protective equipment (PPE) inspection. Results from the course module implementation show that students gained a basic understanding of AI fundamentals after the module, such as dataset annotation, model development, deployment, and evaluation. Qualitative feedback indicates students were motivated to explore further AI-related topics in construction, and several topics that are of their interest were identified. These findings affirm the effectiveness of the proposed module and offer valuable insights for further development and enhancement of AI-related modules in construction education. 

Drones are increasingly being utilized in the construction industry, offering a wide range of applications. As these drones have to work with or alongside construction professionals, this integration could pose new safety risks and psychological impacts on construction professionals. Hence, it is important to understand their perceptions and attitudes towards drones and evaluate the cognitive demand of working with or near drones. Limited research has explored individuals' perceptions of drones, particularly when engaged in construction activities at job sites. This study specifically targets construction students, the future professionals in the field, to understand their responses to drone interactions on job sites. An immersive virtual reality construction site was developed using a VR game engine, allowing construction students to interact with drones while engaging in typical construction activities. Through a user-centered experiment, the influence of drone presence on construction students' attitude, cognitive workload, and perceived safety risk was evaluated. The results suggest that presence of drones did not significantly elevate cognitive load or foster significantly negative attitudes among construction students. Instead, they perceived only mild safety risks, suggesting a general acceptance and adaptability towards drone technology in construction settings.