British University in Dubai

The literature on generative “Artificial Intelligence” (AI) in education primarily focuses on its immediate benefits and applications, such as personalized learning, student engagement, and content generation. However, there is a notable absence of empirical research concerning the holistic use of generative AI within educational institutions and its long-term impact on educational sustainability. This study investigates the factors that predict the use of generative AI and its subsequent influence on educational sustainability. This study enhanced the extended “Unified Theory of Acceptance and Use of Technology” (UTAUT-2) by including personal innovativeness as an external variable and educational sustainability as an outcome. A hybrid methodology, integrating “Structural Equation Modeling” (SEM) and “Artificial Neural Network” (ANN), was used to evaluate the research model using data collected from 1,011 university students. The SEM analysis confirmed all hypotheses. Specifically, a positive relationship was found between use behavior and educational sustainability. These results show that the combined variables collectively account for 63% and 66% of the variance in use behavior and educational sustainability, respectively. The sensitivity analysis identified habit as the most important predictor of use behavior. The findings offer both theoretically important and practically valuable implications for designing user-centered AI integration in education.

This study examines how differences in nation brand strength affect trade between two countries, how it influences the association between geographic distance and trade, and how it impacts the effects of trade agreements on trade. This study uses panel data on export and import flows between the United States and its 36 major trading partners from 1993 to 2016. A gravity model is developed using a first-order Taylor approximation of multilateral resistance terms and estimated by OLS and PPML. The paper constructs a Nation Brand Distance (NBD) measure, measuring the degree to which nation brand strength scores differ between the United States and its trading partners. NBD is calculated based on differences in the Country Brand Strength Index (CBSI) developed by Fetscherin (2010), consisting of per capita values of exports, tourism, foreign direct investment, immigration, and the government environment. The NBD enters the gravity model by itself and through NBD-geographic distance and NBD-trade agreement interaction terms. The findings suggest that NBD mitigates the negative impact of geographic distance on trade, implying that NBD is a significant factor in explaining bilateral trade and overcoming geographic distance. Moreover, NBD mitigates the positive influence of free trade agreements on exports. This means that free trade agreements are less effective when the NBD between trading partners is significant. Countries must develop their nation’s brand strength to enhance trade. Policymakers should prioritize the development of a strong nation brand to make trade agreements more effective and help overcome the barriers of geographic distance. This means that trade policy strategies should integrate different nation branding initiatives, such as public diplomacy, cultural exchanges, and promoting a positive country image abroad. This study contributes to international trade research, particularly to the stream of New Trade Theory (NTT) studies on different types of distances affecting trade. We introduce a new type of distance, the NBD, to complement the development of NTT. JEL Classification: F14

The speech-based chatbot plays a vital role in advancing equality by providing students with timely, equitable, and cost-efficient academic advices. Moreover, the speech chatbot assistant has enhanced its ability to mimic human interaction by integrating state-of-the-art artificial intelligence models, making interactions faster, more engaging, and requiring less effort compared to text-based chatbots. High school students usually experiencing more stress concerning their future careers compared to other students. This situation creates challenges for both students and academic institutions in providing effective and equitable support, especially for schools that cannot afford to hire academic advisers. To address these challenges, we propose a speech-based chatbot named ‘HSGAdviser’ designed to support high school students equally and efficiently. We integrated this chatbot with the pre-trained models from Automatic Speech Recognition (ASR) such as Google Cloud Speech-to-Text, IBM Watson and DeepSpeech instead of training our own model on an audio corpus, as the latter is more time-consuming and resource-intensive compared to using a pre-trained model. We implemented an encoder-decoder architecture utilizing a Bidirectional Long Short-Term Memory (BiLSTM) for the neural network layer. This model incorporates an embedding layer with a 50-dimensional vector, where each word in the vocabulary list is mapped to this vector. This approach enables capturing similarities in meaning and context between words, thereby reducing memory usage and enhancing training performance, making the model more efficient and sustainable. Accordingly, this chatbot can provide additional contributions to sustainable education, including supporting students with 24/7 support services, offering an affordable academic adviser to schools, and replying to students in real-time.

In the educational field, teacher well-being significantly impacts both educators and students. This study, conducted in a private school in Dubai, explores factors affecting teacher well-being, including leadership, job satisfaction, organizational culture, and the work environment. The research involved 65 teachers across various grade levels and two senior leaders, using a mixed-method approach. Quantitative data from a 39-question survey indicated that most teachers were satisfied with leadership and maintained a healthy lifestyle. However, stress and burnout were prevalent due to workload and deadlines. Qualitative interviews with the principal and vice principal highlighted leadership practices that emphasized teamwork and addressed teachers’ concerns. Despite a small sample size and limited generalizability, the study underscores the importance of supportive leadership in enhancing teacher well-being. Utilizing theories such as social cognitive and subjective well-being, the research concludes that inadequate salaries and benefits, coupled with high workloads, negatively impact teachers. This paper provides insights into improving the work environment for teachers, contributing to their well-being and, ultimately, student outcomes.

This systematic study seeks to evaluate the use and impact of transformer models in the healthcare domain, with a particular emphasis on their usefulness in tackling key medical difficulties and performing critical natural language processing (NLP) functions. The research questions focus on how these models can improve clinical decision-making through information extraction and predictive analytics. Our findings show that transformer models, especially in applications like named entity recognition (NER) and clinical data analysis, greatly increase the accuracy and efficiency of processing unstructured data. Notably, case studies demonstrated a 30% boost in entity recognition accuracy in clinical notes and a 90% detection rate for malignancies in medical imaging. These contributions emphasize the revolutionary potential of transformer models in healthcare, and therefore their importance in enhancing resource management and patient outcomes. Furthermore, this paper emphasizes significant obstacles, such as the reliance on restricted datasets and the need for data format standardization, and provides a road map for future research to improve the applicability and performance of these models in real-world clinical settings.

In light of the increasing prevalence of railway ticketing fraud, it is imperative to implement advanced technological solutions to guarantee the integrity and security of ticket transactions. The blockchain technology's decentralized, transparent, and immutable record-keeping method has the potential to address these problems. The utilization of blockchain technology in railway ticketing systems is being analyzed with the aim of enhancing the detection and prevention of fraudulent activities. Smart contracts ensure the security of each transaction by enabling quick verification and minimizing issues such as ticket duplication, illegal modifications, and counterfeit tickets. A blockchain system guarantees the genuineness and validity of tickets for passengers, railway personnel, and ticketing agencies. The report also explores the incorporation of blockchain technology into rail ticketing systems as an adaptable and robust remedy for fraud. Resolving challenges related to the deployment of scalability, data protection, and regulatory compliance. The study conducts a simulation and analysis of the blockchain-based system, revealing a substantial reduction in fraudulent activities. The findings of our study demonstrate that the system effectively eradicates ticket fraud, enhances operational efficiency, and is financially feasible with an adoption cost of $3.54. This enhances the efficiency and reliability of train ticketing.

Motivation Developing countries' reliance on foreign capital for large‐scale infrastructure projects makes sovereign risk premium and debt‐side governance practices key determinants of cross‐border infrastructure risk premiums. Purpose This study estimates the effect of international sovereign bond spreads (systematic risk) and debt‐side governance (unsystematic risk) on cross‐border infrastructure risk premiums in Kenya's major infrastructure projects from 2011 to 2020. Approach and methods We use pooled and random‐effects panel data analysis of secondary data. Findings The findings show that rising international sovereign bond spreads (ranging from 9.6% to 32.39%), corruption levels, external debt‐to‐import ratios, loan utilization rates, disbursement delays, and climate risk disclosure significantly contributed to increasing cross‐border infrastructure risk premiums. The interaction between bond spreads and corruption had a compounding effect in increasing cross‐border infrastructure risk premiums. On the other hand, longer loan maturities, higher internal rates of return, substantial government involvement, and a rising external debt to total investment ratio reduce project risk premiums. Policy implications These findings underscore the need for Kenya's modern Public Debt Management Office and infrastructure execution institutions to reduce external borrowing costs through governance reforms that improve transparency, project oversight, and environmental standards. By strengthening debt‐side governance, Kenya can reduce its external borrowing costs and improve the sustainability of infrastructure‐led debt. As such, the study offers actionable insights for low‐ and middle‐income countries, emphasizing the role of modern sovereign debt management tools that target sustainability and strategic governance reforms at the project level in attracting more favourable borrowing rates for infrastructure financing.

The proliferation of digital learning platforms has revolutionized the generation, accessibility, and dissemination of educational resources, fostered collaborative learning environments and producing vast amounts of interaction data. Machine learning (ML) algorithms have emerged as powerful tools for analyzing these complex datasets, uncovering patterns and trends that offer deeper insights into student performance and engagement. This systematic review examines the application of ML models in e-learning, synthesizing current research findings, methodologies, and challenges. Key contributions include the categorization of ML models based on their applications, an analysis of their predictive accuracy in forecasting student performance and engagement, and the identification of critical data types and sources that enhance model effectiveness. The study highlights ML's transformative potential in personalizing educational experiences, enabling targeted interventions, and improving learning outcomes. Furthermore, it explores the role of ML in facilitating data mining activities, predictive algorithms, and outcome-driven educational strategies within diverse online learning environments. By addressing gaps in the literature, this review not only underscores the practical implications of ML in e-learning but also identifies future research directions aimed at advancing the integration of ML technologies in educational systems. These insights provide a foundation for educators, researchers, and technologists to harness ML for enhancing teaching and learning processes.

The main purpose of the chapter is to investigate human capital empowerment within the Gulf Cooperation Council (GCC) healthcare sector, accentuating its role in nurturing a sustainable digital circular economy. The chapter identifies barriers to effective human capital development and assess the impact of such challenges on healthcare outcomes in the GCC. The chapter employs a comprehensive literature review, synthesizing prior and contemporary research to contextualize human capital empowerment in the GCC healthcare setting. It highlights the relevance of strategic leadership, continuous learning and interdisciplinary collaborations to address ongoing workforce challenges. Preliminary findings reveal significant barriers to human capital empowerment in the form of accelerated turnover rates and a severe dearth of trained local medical professionals. It underscores the imperativeness for robust, ongoing training programs with a cultural shift towards acknowledging and valuing continuous learning to enhance operational efficiency and patient care quality in the GCC healthcare sector. This chapter contributes to sustainable human capital development literature by concentrating on the unique challenges faced by the GCC healthcare sector. It integrates concepts of digital innovation and sustainability, offering a dynamic and novel perspective on how these elements can enhance workforce capabilities and resilience. The findings suggest critical pathways and vital directions for policy development aimed at reinforcing and strengthening human capital in the GCC healthcare system. Future research should investigate context-specific strategies to overcome identified barriers and assess the long-term impacts of human capital initiatives on healthcare outcomes and sustainability.

Sustainable engineering builds systems, products, and processes that are socially, environmentally, and economically viable to fulfil the promise of a balanced approach to achieve the net zero emission targets of the world to mitigate climate change impacts. Owing to the multidisciplinary nature of sustainable development, sustainability efforts involve concepts, principles, and methods from engineering, social sciences, economics, social psychology, biological sciences, ecology, and physical sciences. Hence, scientific analysis is required to define inter-item relationships and identify differences based on the demographic features of professionals. The lack of such studies in the literature represents the main gap covered by this study. In this context, a methodology was designed and applied by surveying 101 professionals from various engineering disciplines in the UAE’s construction sector. The results confirmed a significant correlation among sociocultural (SOC), economic (ECO), and environmental (ENV) sustainability factors. The findings revealed that the distributions of SOC, ECO, and ENV were the same across gender, specialisation, and experience categories. Moreover, the distributions of ECO and ENV were the same across age categories, except for the distribution of SOC, which differed across age categories, favouring groups over 25 years of age. These findings would support stakeholders in the construction sector in developing sustainable engineering building systems. The proposed methodology can be used in other areas to help stakeholders establish sustainable systems based on the SOC, ECO, and ENV factors.

The rapid rise of Generative AI in education has brought transformative potential. However, there is limited empirical insight into the factors influencing students’ use of these tools and their impact on academic performance. Specifically, research has not thoroughly examined how task-technology fit and behavioral factors shape Generative AI usage. This study addresses these gaps by integrating the Task-Technology Fit (TTF) and the Theory of Planned Behavior (TPB) to develop a theoretical research model. Data were collected from university students through a structured survey, and the model was validated using a hybrid Structural Equation Modeling-Artificial Neural Network (SEM-ANN) approach. The results demonstrate that both task and technology characteristics significantly impact task-technology fit, positively influencing the use of Generative AI tools. Additionally, behavioral factors such as attitudes, subjective norms, and perceived behavioral control were found to strongly encourage Generative AI usage. Notably, the study confirms that these AI tools positively contribute to students’ academic performance. At the same time, the study recognizes the ethical dilemmas tied to Generative AI, highlighting issues such as academic integrity, excessive dependence, and its potential effects on critical thinking. The findings offer valuable insights for various stakeholders and provide practical guidance for strategically integrating AI tools to enhance student outcomes.

Large language models (LLMs) have made significant advancements in natural language processing (NLP), impacting both academia and industry. Evaluating LLMs is crucial, as these models are developed for multiple NLP tasks. However, no single LLM has successfully fulfilled all tasks simultaneously, creating a research gap. This, in turn, leads to the identification of the most and least effective LLMs in real-world problems, presenting a multi-criteria decision-making (MCDM) challenge due to the diversity of evaluation tasks, task prioritization, data variability, and issues related to ranking and grading with binary data. While the three-way decision (3WD) approach based on MCDM methods can address this, it often leaves uncertainty as an open issue, highlighting a theoretical gap. To address this, the contribution of this study is the development of a new 3WD approach based on utility and dynamic localization transformational procedures within a circular q-rung orthopair fuzzy set (C-q-ROFS) for ranking and grading LLMs. The methodology includes (1) reformulating the fuzzy weighted zero inconsistency-based interrelationship process (FWZICbIP) using C-q-ROFS (C-q-ROFS–FWZICbIP method) to prioritize tasks and address weighting uncertainty; (2) formulating a decision matrix by intersecting LLMs with NLP tasks while applying utility and dynamic localization procedures to handle binary input issues; and (3) reformulating the conditional probabilities by opinion scores (CPOS) method within the C-q-ROFS context (C-q-ROFS–CPOS method) to determine decision thresholds for each LLM. This involves incorporating Bayesian decision theory under C-q-ROFS to establish decision thresholds for all LLMs, thereby enhancing the certainty and effectiveness of the grading process. Based on this, the 3WD approach is developed to offer a robust mechanism for ranking and grading LLMs. Forty LLMs were ranked and graded across 11 NLP tasks, with the findings showing that LLM14 demonstrated high efficacy, ranking in the positive region for nine σ values, but falling into the boundary region at σ = 0.05. Sensitivity and comparison analyses were conducted to evaluate the robustness and stability of the methodology.

The integration of technology in education is essential for enhancing learning experiences and preparing students for a technology-driven world. However, many teachers lack the necessary skills and confidence to effectively incorporate technology into their classroom practices. This paper investigates the impact of technology-related professional development (PD) on teachers' self-reported technology competencies and their application of technology in classroom practices, addressing the critical need for effective PD programs that can bridge this gap. The study involved the development and administration of a self-reported Information and Communication Technology (ICT) competencies survey, the observation of classroom practices using a custom-developed observation tool, and the implementation of a comprehensive PD program followed by a subsequent reassessment using the same instruments. The findings demonstrate significant improvements in teachers' self-reported competencies and classroom practices following the PD sessions. The validated self-rating technology skills survey and classroom observation tool proved to be reliable and effective measures for assessing teachers' ICT competencies and the integration of technology in teaching. The study underscores the necessity of well-designed PD programs that incorporate active learning, collaboration, and sustained support to enhance teachers' technology integration skills.

Due to Jordan’s paucity of natural resources, there are various environmental challenges that make it difficult to maintain population increase. It is anticipated that Jordan, one of the nations with the fewest natural resources, would slip below the poverty line. This study addresses the most pressing environmental concerns confronting Jordan, including water constraint, pollution, and desertification. The topic of water shortage is then discussed, along with the environmental repercussions of current practices and proposals for improvement. This study describes a “sustainability package” that may help avert these problems and establish a sustainable future. Governmental and non-government organizations are closely engaged in environmental concerns to build a solid foundation for environmental protection. Citizens and entities should collaborate to discover realistic answers to the nation’s concerns. The primary goal of this research is to establish a framework for environmental water preservation in Jordan, ensuring that water resources are managed and utilized in a sustainable, egalitarian, and health-protective manner. This includes balancing the demands of various water users, safeguarding water quality, and preserving water supplies for future generations. It is feasible to encourage water conservation, enhance water management practices, and safeguard water resources for future generations by establishing such a framework for environmental water preservation in Jordan. This may help guarantee that there is enough water to fulfil people’s and the environment’s requirements, and that aquatic ecosystems stay healthy and resilient in the face of increasing demands from human activities.

Comprehending intermarket relationships among asset classes/commodities and the changing dynamics among the gold, bitcoin, and oil markets under high or low-volatility indexes is now imperative for investors. This paper presents a qualitative study to elicit expert views on the relationships between two major commodities (gold and oil) and bitcoin, specifically emphasizing the pre- and post-COVID-19 era. The thematic analysis of 30 finance experts revealed gold as a safe haven and portfolio diversifier; however, it has lost importance as an inflation hedge post-COVID-19 (2020–2022). Moreover, findings indicated that bitcoin was not a substitute for gold and that there was a positive correlation between gold and oil and the gold volatility index (VIX). Furthermore, there was a negative correlation between the oil VIX and the bitcoin VIX, with no correlation between the gold–bitcoin or oil–bitcoin nexus. These findings are pertinent for investors and scholars in the context of portfolio allocation/portfolio design that comprise these vital asset classes/commodities.

Purpose The theoretical landscape surrounding the contribution of digital transformation to sustainability in higher education institutions is lacking in literature. Blended learning has gained popularity and poises for further growth as a sustainable and inclusive mode of learning that will shape the future of education. This study aims to investigate the organizational critical success factors that ensure high-quality blended learning opportunities. Design/methodology/approach Data was collected through an online student survey and semistructured interviews with academic leaders and faculty members. Findings Exploratory factor analysis and multiple linear regression revealed five main contributing factors to a successful overall hybrid experience, namely, faculty support, cognitive flexibility, learner self-actualization, student engagement and sense of belonging. In the results, students were satisfied with their gained skills, knowledge and engagement, and have succeeded in developing cognitive flexibility, self-actualization and sense of belonging. Faculty support was the strongest determinant. The presence of certain organizational dynamics, comprising management support of those with sustainability mindset, effective communication, blended leadership qualities and adequate faculty personality traits, presents as a major predictor to quality learning opportunities. Originality/value The theoretical landscape surrounding the contribution of digital transformation to sustainability in higher education institutions is lacking in literature, which emphasizes the novel aspects of this study. In particular, it contributes by determining the overall level of research on the subject, theoretical stances in this area and potential avenues for further investigation.

This research examines K-12 STEM educators' viewpoints on incorporating ChatGPT within their teaching practices. The study is grounded in two theoretical models: the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technological Pedagogical Content Knowledge (TPACK) framework. Through semi-structured interviews with nine K-12 STEM teachers in the UAE, the research identifies three main themes: ecosystem support, ease of use, and job enhancement through ChatGPT. Teachers generally appreciate ChatGPT's potential to offer personalised learning experiences, enhance their instructional practices, and reduce logistical burdens. However, the study also uncovers several barriers, including misconceptions about ChatGPT's capabilities and a significant need for professional development in AI education. Educators also raised issues regarding data privacy, the accuracy of responses, and the possibility of reducing students' ability to think critically. The research highlights the importance of developing comprehensive training initiatives to prepare educators to successfully incorporate ChatGPT into their instructional methodologies. The recommendations suggest designing extensive professional development initiatives and conducting additional research to assess the long-term effects of ChatGPT on both teaching and learning processes. By tackling these challenges, the study seeks to support the responsible and efficient implementation of AI tools in K-12 education.

In the era of rapid technological advancement, generative artificial intelligence (AI) has emerged as a transformative force in various sectors, including environmental sustainability. This research investigates the factors and consequences of using generative AI to access environmental information and influence green purchasing behavior. It integrates theories such as the information adoption model, value-belief-norm theory, elaboration likelihood model, and cognitive dissonance theory to pinpoint and prioritize determinants of generative AI usage for environmental information and green purchasing behavior. Data from 467 participants were analyzed using a hybrid methodology that blends partial least squares (PLS) with artificial neural networks (ANN). The PLS outcomes indicate that interactivity, responsiveness, knowledge acquisition and application, environmental concern, and ascription of responsibility are key predictors of generative AI use for environmental information. Furthermore, environmental concerns, green values, personal norms, ascription of responsibility, individual impact, and generative AI use emerge as predictors of green purchasing behavior. The ANN analysis offers a unique perspective and discloses variations in the hierarchy of these predictors. This research provides valuable insights for stakeholders on harnessing generative AI to promote sustainable consumer behaviors and environmental sustainability.