The construction industry plays a key role in economic development but continues to face challenges in promoting employee well-being, particularly mental health and social sustainability. While existing decision-making tools emphasize environmental and economic factors, the social dimension remains largely overlooked, creating a significant gap in both research and practice. To address this, the study develops a decision support tool (DST) to help construction organizations prioritize strategic investments that enhance employee social sustainability. The tool is based on a hybrid multi-criteria decision-making framework, combining the Analytical Hierarchy Process (AHP) with Fuzzy MOORA to integrate both quantitative and qualitative assessments. A literature review, along with findings from a previous empirical study, identified 27 validated criteria, grouped into seven core sustainability alternatives. Additionally, five decision criteria (cost, risk, compatibility, return on investment, and difficulty) were refined through expert interviews. The DST was implemented as a modular Excel-based tool allowing users to input data, conduct pairwise comparisons, evaluate alternatives using linguistic scales, and generate a final ranking through defuzzification. A case study in a private construction company showed Training and Development and Work Environment as top priorities. An online expert focus group confirmed the DST’s clarity, usability, and strategic relevance. By addressing the often-neglected social pillar of sustainability, this tool offers a practical and transparent framework to support decision-making, ultimately enhancing employee well-being and organizational performance in the construction sector.

Generative Artificial Intelligence (GAI) is rapidly transforming higher education, yet its integration within Mechanical Engineering Education (MEE) remains insufficiently explored, particularly regarding the perspectives of faculty and students on its enablers, challenges, strategies, and psychological dimensions. This study addresses this gap through a sequential mixed-methods design that combines semi-structured interviews with faculty and students, along with a large-scale survey (N = 105) compromising 61 students and 44 faculty members primarily from universities in the UAE. Quantitative analyses employed the Relative Importance Index (RII) to prioritize factors, Confirmatory Factor Analysis (CFA) to test construct validity, and Partial Least Squared Structural Equation Modeling (PLS-SEM) to examine interrelationships. Results indicate convergence across groups: the top enablers include students’ willingness and tool availability for time efficiency; the main challenges concern ethical misuse and over-reliance reducing critical thinking; and the most effective strategies involve clear policies, training, and gradual adoption. CFA confirmed construct reliability after excluding low-loading items (SRMR ≈ 0.11; RMSEA ≈ 0.08; CFI ≈ 0.70). PLS-SEM revealed that enablers, challenges, and strategies significantly influence overall perceptions of successful integration, whereas psychological factors exert no significant effect. The study offers empirically grounded priorities and validated measures to guide curriculum design, faculty development, and policy formulation for the responsible and effective adoption of GAI in MEE.

The construction sector holds a paramount position in the economic landscape of any country, serving as its foundational pillar. This sector, characterized by its diverse and dynamic environment, is crucial in job creation across various domains, including transportation, real estate, manufacturing, trade, warehousing, wholesale, and leasing services. Employing about one quarter of the global workforce, its significance is undeniable. Despite its pivotal role, the construction sector grapples with significant mental health and social sustainability challenges. Reports in recent years indicate that approximately one in four individuals worldwide experiences various forms of mental disorders. A study by the Global Burden of Disease in 2010 revealed that around 400 million people globally suffer from depression with projections suggesting that depression could be the leading cause of employee mortality by 2030. This underscores the critical need to address mental health and well-being issues in this sector. While the existing literature has presented numerous studies and reliable scales linking employee mental health and well-being to factors such as job satisfaction, productivity, absenteeism, and low turnover rates, these studies often operate in isolation, concentrating on specific aspects of mental health. This study views mental health and well-being as essential parts of defining social sustainability as a comprehensive concept. Moreover, limited research has been conducted to assist organizations in decision making and facilitate efforts to enhance the social sustainability of employees in the construction sector, highlighting a noticeable research gap. To address this gap, our study adopted a comprehensive mixed-methods approach, incorporating semi-structured interviews, surveys, and structural equation modeling to identify the underpinning criteria that define the social sustainability of employees. This study accordingly incorporated the identified criteria to evaluate the relationship and impact of these factors on employees’ job satisfaction, ultimately contributing to the assurance of social sustainability for employees within the construction sector in the UAE. This holistic approach seeks to establish the intricate relationship between employees’ job satisfaction and their mental health, providing valuable insights for guiding organizational decisions and fostering improvements in employee social sustainability in the construction sector generally and the UAE construction sector in particular.