Integrating Artificial Intelligence into Construction Curricula Insights from University Faculty in Thailand

Abstract

The Architecture, Engineering, Construction, and Operations (AECO) industry is undergoing a rapid transition toward the era of Artificial Intelligence (AI). However, empirical evidence regarding AI integration in Thailand's AECO curricula remains limited. This study aims to analyze the perspectives and assess the readiness for AI integration using a survey research methodology. Data were collected from a nationwide sample of 87 AECO faculty members. The research instrument was a questionnaire with a Cronbach’s Alpha reliability coefficient of 0.92. The findings indicate that faculty members strongly agree with AI integration at the highest ( = 4.52, SD = 0.48), with Analytical and Predictive AI skills being the most emphasized areas. The research significance suggests that educational institutions must urgently improve infrastructure and instructor competencies to align with the Thailand National AI Strategy and Action Plan.

The aim of this study was to assess the level of importance of artificial intelligence (AI) applications and to examine the levels of expertise across different competency categories among faculty members teaching in construction-related programs in Thailand. In addition, the study prioritizes the key driving and sustaining factors that influence AI integration into curricula in order to propose policy-oriented recommendations for higher education development aligned with the National Artificial Intelligence Strategy (2022–2027). An online questionnaire was employed to collect data from 87 faculty members across higher education institutions offering programs in civil engineering, architecture, industrial education, and construction management. The collected data were analyzed using descriptive statistics, including mean () standard deviation (SD) and effect size analysis to determine practical significance.

The findings indicate that faculty members place a high level of importance on AI integration in teaching and learning, particularly in the domain of analytical and predictive AI, which is mainly associated with the language processing and document management that support research and data-driven practices, which reached a high level with the highest average score ( = 3.70, SD = 1.03). Most respondents reported an AI proficiency level at L3, accounting for 34.48%, indicating the ability to integrate AI tools into instructional activities. Among the driving factors, the need for clear ethical governance and regulatory frameworks for AI emerged as the most influential ( = 4.24, SD = 0.89), while curriculum flexibility in response to rapid technological change was identified as the most critical constant factor ( = 4.33, SD = 0.74), these results highlight. Effect size analysis revealed that dimensions of labor market demand and technological velocity exhibited the highest practical significance (d = -0.41), reflecting that external pressures and technological acceleration act as structural conditions with a more profound impact than mere adaptation incentives. Dimensions concerning budget, resources, and curriculum improvement showed small effect sizes (d = -0.20 to -0.25), indicating that structural constraints are more distinct obstacles than policy-driven catalysts. Conversely, institutional and national policies showed a negligible difference (d = 0.05).

These findings emphasize the necessity of a systemic approach, connecting government, universities, and industry to create clear ethical guidelines and budgetary support, ensuring sustainable AI integration that responds to the evolving demands of the digital era.