2025 ASEE Annual Conference & Exposition

The integration of Artificial Intelligence (AI) in the construction industry is gaining momentum, driven by its potential to enhance project efficiency, safety, and innovation. However, the successful adoption of AI technologies relies heavily on the ability of construction professionals to understand, implement, and manage these new technologies. Despite the growing presence of AI, a significant gap remains in the preparedness of the workforce to effectively utilize these technologies. To bridge this gap, this paper aims to identify the specific learning needs and educational requirements of construction professionals regarding AI.
This paper presents the findings from a research study that aims to quantify the learning needs of construction professionals in relation to the concepts AI. Utilizing a survey-based methodology, data was collected from a diverse range of construction professionals, including project managers, engineers, site supervisors, project administration staff, and other stakeholders across various sectors of the industry. The survey and accompanying questionnaires were designed to assess the current level of AI awareness , familiarity with AI applications, and the perceived learning needs of AI for day-to-day construction activities . Furthermore, the study explores the respondents' willingness and interest in participating in AI-focused training programs, as well as the barriers they face in acquiring such knowledge.
The quantitative data analysis reveals that while a majority of professionals recognize the potential benefits of AI, there are widespread gaps in knowledge and technical skills. Many respondents reported limited exposure to education of AI tools and techniques, particularly in areas like machine learning, predictive analytics, and automation. This lack of familiarity has led to a cautious approach toward adopting AI, with professionals citing concerns about the complexity of AI systems, the cost of implementation, and the need for specialized training. Additionally, the study highlights differences in AI learning needs across job roles, with project managers, for example, expressing a greater need for training in AI-driven project management tools, while engineers and site supervisors indicated a need for more hands-on experience with AI-powered safety and quality control systems.
One of the key findings of the research is the demand for structured, industry-specific AI training programs tailored to the construction sector. Respondents emphasized the importance of practical, application-oriented training that addresses real-world challenges faced in construction projects. There was also a strong preference for flexible, accessible learning formats, such as online courses, workshops, and certification programs, that would allow professionals to upskill without interrupting their work schedules. Additionally, the study found that collaboration between academia, industry organizations, and technology providers is crucial in developing and delivering these AI education and training programs.
This paper concludes by discussing the implications of these findings for educators, industry leaders, and policymakers. It advocates for the development of targeted educational initiatives that focus on equipping construction professionals with the necessary AI skills. By addressing the specific learning needs of the workforce, these initiatives can accelerate the adoption of AI technologies in the construction industry, ultimately driving improvements in productivity, safety, and innovation. The findings of this research highlight a clear need for comprehensive, accessible AI education and training for construction professionals. The findings of this research are expected to provide valuable insights into the areas where knowledge gaps exist and offer a framework for developing effective learning strategies that can prepare the construction workforce for the future of AI-driven construction.