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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 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.

Despite global efforts to reduce greenhouse gas emissions, the energy sector remains a major contributor, with hydrocarbon-based resources fulfilling around 80% of energy needs. As such, there is a growing focus on identifying effective and economically feasible policy mechanisms to promote renewable energy adoption. This study focuses on the theoretical problems surrounding the adoption of renewable energy policies. The study aims to highlight the potential for sustainable growth using renewable energy in the UAE and identify the most viable policy mechanisms for enhancing grid-tied solar energy adoption using qualitative and quantitative data collection methods and the HOMER Grid software. Compared to previous research, this study contributes by identifying a unified renewable energy policy mechanism that could significantly enhance the adoption of grid-tied solar energy generation in the UAE. The study’s main findings show that a unified renewable policy mechanism could enhance grid-tied solar energy adoption throughout the UAE’s electricity authorities. Net metering emerges as the most efficient and economically viable policy for customers and electricity utilities.

Carbon emissions, being hazardous, are triggering social concerns which have led to the creation of international treaties to address climate change. Similarly, the United Kingdom under the Climate Change Act (2008) has committed to reducing its greenhouse gas emission by at least 80% over 1990 levels by 2050. However, being the oldest member of the EU states (before Brexit), the UK has the oldest housing stock, which contributes to 45% of its carbon emissions due to the older dwellings. To address this issue low carbon retrofitting is needed. Therefore, this paper seeks to investigate the barriers and enablers to energy efficiency retrofitting in social housing in London, UK based on the perception of experts employed in National and construction companies with an experience that ranges between 6 to 16 years. Initial literature suggested that the problem of energy efficiency retrofitting in the general building stock has been addressed, however little has been reported on its application to social housing. This paper, therefore, groups the barriers and enablers into seven categories that include: financial matters, Technical, IT, Government policy and regulation, social factors (including awareness of the energy efficiency agenda), quality of workmanship and disruption to residents, using literature review, interviews and surveys with key stakeholders within the housing sector, and draws recommendations to enable effective and efficient retrofitting for social housing projects.

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.

Sustainability has gained popularity and importance around the globe due to the ever-increasing effects of climate change and global warming on Earth. As of the 21st century, human endeavour has caused an enormous amount of damage to the environmental ecological system. Among which, one of the major contributors to the increase in the environmental issues and CO2 emissions are the conventional sources of energy, especially in the built environment. Globally, the built environment accounts for 12 percent of the world's drinkable water, 40 percent of energy wastage and 35 percent of scarce natural resources, which in turn produces 40 percent of the total global carbon emission. Among which are educational buildings which tend to be a major contributor (as most of these facilities are old and conventionally built in the mid 1900's) Thus, with the education sector being an essential part of society, it becomes important to determine the energy performance and carbon footprint of these buildings. The United Arab Emirates (UAE) vision 2021 highlights the country's approach to the importance of providing the best education and adopting sustainable environmental infrastructure. Therefore, this study adopts a methodological approach based on semi-structured interviews and surveys, in order to compare the energy performance of three educational buildings within Higher Education establishments in the UAE as a case study. The study also evaluates the end user's awareness of the importance of sustainable practices in the buildings and their preference of these buildings. The findings of this study conclude that Net Zero Energy Buildings (NZEBs) are the most efficient buildings in terms of energy performance, carbon consumption and heat generated. Therefore, it is important that the integration of these types of buildings are considered in educational establishments.

There is no doubt that the sudden migration to online learning amid COVID-19 has caused a degree of stress to students and faculty in higher education globally. Although several studies have reported on the challenges that faced the academic world during the pandemic and lessons learnt from some of the practices adopted through the delivery of online education, limited studies have been conducted to look into the most significant factors that impact students’ perception of the online delivery amid COVID-19. As such, this paper is based on a quantitative study targeting a highly rated higher education institution in the United Arab Emirates (UAE), to identify the most significant pedagogical, psychological, and technological factors that impacted on students’ online learning experience amid COVID-19 and model the relationship between these factors and students’ perception of successful online learning, using structural equation modeling (SEM). The study also investigates the relationship between the students’ background and their satisfaction with the online delivery of teaching amid COVID-19. In addition, RII (relative importance index) analysis is used to identify the most important factors that impact students’ satisfaction with the online delivery. The study concludes that there are at least twenty-six factors that impact on students’ perception of successful online delivery, and that there is a positive relationship between the instructors’ teaching style and use of technology and the students’ perception of successful online delivery.

In recent times, smart cities and sustainable development have drawn significant research attention. Among developed and developing countries, the United Arab Emirates (UAE) has been at the forefront in becoming an incubator for smart cities; in particular, it has placed some efforts in the education sector by transforming the traditional campus into a Smart Campus. As the term Smart Campus attracts professionals and academics from multiple disciplines, and the technology keeps intervening in every aspect of life, it becomes inevitable for the Smart Campus to take place and deploy the future vision of smart cities. As a first step to achieve this vision, it is very important to develop a clear understanding of what is a Smart Campus. To date, there is still no clear perception of what a Smart Campus would look like, or what are the main components that can form a Smart Campus. Therefore, the objective of this research is to use the set of comprehensive criteria to identify what it is perceived to be a Smart Campus and evaluate these criteria from the stakeholders’ perception. The main criteria are defined from the literature review, and a case study is conducted on the American University of Sharjah campus stakeholders (faculty, students, management, and Information Technology (IT)) to assess the designated criteria. This exploratory research relies on both qualitative and quantitative methods to perform the analysis, taking into consideration the perceptions of students, faculty, and IT service providers. Finally, having defined and evaluated the criteria that underpin the Smart Campus framework, a set of recommendations are drawn to guide the utilization of a Smart Campus within higher education settings. This research opens the doors for future studies to gain a deeper insight into the type of decisions that need to be made to transform a traditional campus to a Smart Campus.

PurposeEmergency preparedness (EP) is one of the crucial phases of the disaster management cycle for the built environment. The body of knowledge, therefore, reports on different preparedness standards adopted by developed countries such as the United Kingdom (UK), the United States of America (USA), Canada, Japan and Australia. Other countries, however, such as the United Arab Emirates (UAE) (in the absence of its preparedness framework), have long adapted the UK preparedness standards. This has called for this study to investigate the state of EP practices in the UAE to identify the limitations and challenges it has been facing during its preparedness phase when adopting the UK preparedness standards.Design/methodology/approachQualitative methods of data collection and documentation with the content analysis were adopted to identify the barriers faced by the preparedness phase of emergency management (EM) in the UAE. A Pilot study was therefore conducted to validate eight key elements of the EP phase identified from the literature. The state of EP phase and the extent to which the eight key elements of EP elements were practiced and the barriers in their implementation in the UAE were explored through interviews at federal (National Crisis and Emergency Management Authority) and local levels (local team of crisis and emergency management).FindingsThe study identified eight key elements of the EP phase and the associated barriers related to their implementation in the UAE. The barriers were ranked based on their severity by interviewing experts at both federal and local levels.Practical implicationsThis paper addresses the need to investigate the state of the EP phase, its key elements and the barriers faced during its implementation in the UAE.Originality/valueDue to the absence of any EP frameworks or systems in the UAE, this paper aims to validate the EP elements identified by adopting a qualitative approach.

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.