Evaluating AI Adoption and Readiness Among Architecture and Built Environment Students

Abstract

The construction industry is known as a slow adopter of technological change, yet Artificial Intelligence (AI) is increasingly challenging this trend by becoming a major catalyst for innovation. However, the readiness of the construction industry’s future workforce to adopt AI remains unclear. The purpose of this study is to evaluate the readiness and intention of students (i.e., future workforce) to adopt AI in their future professional practice. This study employs a quantitative research design using a structured questionnaire based on the UTAUT 2 framework. The survey was distributed to undergraduate and postgraduate students in civil engineering, architecture, building surveying, quantity surveying, and construction project management programs. The questionnaire includes items measuring key UTAUT 2 constructs such as performance expectancy and effort expectancy, all contextualized to AI technologies. The study identifies some UTAUT 2 constructs that influence students’ readiness and intention to adopt AI in their future careers. Findings also reveal the levels of AI use, exposure, and awareness among students. This study relies on self-reported data, which may be influenced by social desirability bias or limited understanding of AI technologies among students. The study offers valuable implications for curriculum development, industry-academia collaboration, and students’ preparation for future job demands. This research is among the first to apply the UTAUT 2 framework to assess AI adoption specifically within the context of construction students. While existing studies have explored technology adoption in the construction industry, limited work has focused on perceptions and preparedness of students regarding AI integration.