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This study aims to explore how artificial intelligence (AI) tools influence the academic success and adaptation of international students in higher education. It examines the benefits, challenges and ethical considerations including academic integrity of integrating AI in learning environments.

An exploratory qualitative research approach was employed, utilising semi-structured interviews with postgraduate international students from diverse backgrounds.

The findings suggest that AI tools enhance academic performance by offering personalised learning, immediate feedback and efficient assessment. However, concerns about ethical use, over-reliance and the potential impact on critical thinking and academic integrity were prominent in the contexts of assessments and learning experiences.

The research offers unique insights by focusing on postgraduate international students, an often-underrepresented group in AI education studies. Their distinctive challenges, including adapting to new academic environments and overcoming language barriers, make them a particularly valuable sample for understanding the role of AI in higher education. This focus allows the study to contribute new perspectives on how generative AI (GenAI) tools like Grammarly and ChatGPT facilitate academic performance improvement, especially in enhancing writing proficiency and managing academic expectations. These findings extend the discussion by specifically addressing the experiences of international students in postgraduate studies, a demographic where AI’s impact has been less explored.

This paper aims to investigate the sentiment connectedness among 10 European stock markets between January 2020 and July 2022, associating such connectedness with the level of the geopolitical risk index.

For this purpose, a time-varying parameter vector autoregressive connectedness framework is used.

Results show a high degree of sentiment connectedness. Overall, the sentiments of Portugal, France, the Netherlands, Spain, Germany and Italy are net transmitters of shocks while those of Poland, Sweden, Norway and Romania are net receivers. Additional evidence indicates that when geopolitical risks increase, the sentiment connectedness tends to decrease. However, the reverse holds under extremely high levels of geopolitical risks.

Overall, this study provides some significant contributions to the literature. First, to the best of the authors’ knowledge, this is among the first few studies to examine the dynamic connectedness among stock market sentiment across countries. This issue needs special consideration for European countries because of their close geographical distance and strong integration due to the European Union’s co-development strategies. Second, the association of sentiment connectedness with geopolitical risk is examined for the first time. This is even more meaningful in the context of growing geopolitical risks stemming from the Ukraine war, which could affect international financial markets.

We aim to examine two issues. First, we intend to identify the best performing expected return proxies. Second, we investigate whether the expected return proxies for individual stocks can track the corresponding realized returns during extremely good or extremely bad times of the economic environment related to business conditions, stock market valuation and broad market performance.

We construct four sets of expected return proxies, including: (1) characteristic-based proxies; (2) standard risk-factor-based proxies; (3) risk-factor-based proxies that allow betas to vary with firm characteristics and (4) macroeconomic-variable-based proxies. First, we estimate expected returns for individual stocks using newly developed methods and evaluate the performance of these expected return proxies based on the minimum variance criterion of Lee et al. (2020). Second, we regress expected return proxies and realized returns on indicator variables that capture the extreme phases of the economic environment. Then we compare the estimated coefficients from these two sets of regressions and see if they are similar in magnitude via formal hypothesis testing.

We find that characteristic-based proxies and risk-factor-based proxies that allow betas to vary with firm characteristics are the two best performing proxies. Therefore, it is important to allow betas to vary with firm characteristics in constructing expected return proxies. We also find that model-based expected return proxies do a reasonably good job capturing actual returns during extremely bad and extremely good phases of business cycles measured by leading economic indicators, consumer confidence and business confidence. However, there is a large gap between the adjustment of model-based expected returns and realized returns during extreme episodes of stock market valuation or broad market performance.

We examine four types of expected return proxies and use the newly developed methodology as in Lee et al. (2020) to see which one is the best. In addition, we document whether model-based expected returns from individual stocks adjust partially or fully to keep pace with actual returns in response to changing economic conditions. No prior studies have examined these two issues.

This research investigates the complex relationship between economic policy uncertainty (EPU), energy consumption and institutional factors in the Gulf region. The purpose of this study is to examine how institutional factors moderate the impact of EPU on energy consumption in Gulf countries.

This paper uses the dynamic panel autoregressive distributed lag (PARDL) method, over a period stretching from 1996 to 2021 in the Gulf countries.

The results show that, only in the long term, EPU has a positive and significant impact on energy consumption, suggesting that increased EPU leads to increased energy use. Furthermore, this study found that, only in the long term, government effectiveness and regulatory quality have positive and significant effect on energy consumption. Accordingly, the two institutional factors play a moderating role in the EPU−energy consumption nexus.

This study highlights the importance of considering the time dimension when formulating energy and economic policies in Gulf countries. Policymakers should take into consideration the nature of these relationships to make informed decisions that promote energy efficiency and economic stability in the region.

To the best of the authors’ knowledge, this is the first study examining the relationship between EPU and energy consumption in the Gulf countries while incorporating the role of institutional factors as potential mediators.

This chapter examines the Asia-Pacific region's circular economy (CE) actions concerning climate change policies.

A systematic narrative synthesis of subsequent systematic literature review (SLR) using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines is adopted, and this study reviews 38 sample studies on CE practices and climate change policies in conducting the survey.

The findings suggest a policy convergence in the CE and climate change. This study identified several gaps in current policies regarding CE integration, such as a lack of comprehensive CE legislation, insufficient incentives for circular practices, limited focus on waste reduction and resource efficiency, need for enhanced public awareness and education, inadequate infrastructure for recycling and waste management and weak coordination among stakeholders. Challenges and barriers were identified regarding economic, social, political and technological aspects.

This chapter provided policy recommendations on integrative approaches, regional cooperation and partnerships and implementing financial and regulatory incentive practices. New technologies and methods open the door to expanding the goal of the CE.

This study presents the findings of the current status of CE actions regarding climate change policies for the Asia-Pacific countries, provides proactive management recommendations and implications for future practices and identifies the need to collaborate for the region's sustainable development.

This study seeks to review the existing literature on the challenges and issues of climate change and sustainability in the South Asian region.

The study adopts the systematic literature review using the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The sample consists of 39 studies highly relevant to the topic.

Population, urbanisation, poverty, inequality and health impacts were identified as socio-economic challenges. In contrast, deforestation and land degradation, water scarcity and quality, and biodiversity loss were identified as environmental challenges in mitigating climate change while achieving sustainability. Nonetheless, national, regional and international sustainability endeavours are still limited in their functionality and applications due to socio-economic inequalities, political problems and weak cohesiveness in the region.

The conclusions call for immediate concern to effective and concerted strategies and policies to address climate vulnerability and improve climate brilliant resilience in South Asia. Policymakers, practitioners and all those concerned with these phenomena are encouraged to utilise integrated strategies that focus on preventing and managing change, using technological innovations and promoting regional and international cooperation.

To the authors' knowledge, the current study differs from prior works because it employs a comprehensive systematic review to capture diverse literature on climate change issues and sustainable development endeavours within South Asia.

With the rise in demand and adoption of smart city initiatives, it is imperative to plan the railway infrastructure, as it will have a huge positive impact if adequately integrated into the planning process. Given the complexities involved, a whole systems thinking framework provides a useful platform for rail transport planners.

This paper proposes a simple, adoptable framework utilising systems thinking concepts and techniques taking into cognisance the key stakeholders. Milton Keynes in the United Kingdom is the adopted case study.

Selected systems thinking tools and techniques are adopted to develop a framework for mapping stakeholders and attributes when developing sustainable rail transport systems, taking note of their core functionalities and the complex systems wherein they exist.

The desire to build future (smart) cities is to effectively match infrastructural resources with a rapidly growing population, and the railway sector can play a strategic role in building a much more competitive low-carbon-emission transport system, which is a driving force for sustainable development.

The urban rail service has become vital to urban development as railway stations serve as hubs for sustainable mobility to meet local requirements. Moreover, it takes extra effort to input railway development into smart city plans, as it is a herculean task to get governments to focus on it with clarity of purpose in passing legislation.

The developed framework reduces complexities when planning and designing rail transport systems compared to many of the existing reductionist planning approaches. The simplicity of the framework would also make it easily adoptable by a wide range of users.

The purpose of this paper is to introduce an aid for teaching transverse flux permanent magnet machines (TFPMs) with emphasis on their torque production.

The Lorentz force law is applied to fictitious current loops emulating the permanent magnets (PMs) mounted on the rotor according to different arrangements; the air gap flux density is created by the armature current.

Implemented in a master lecture on special AC machines, the proposed approach has revealed a renewed interest in electromagnetic fundamentals for pedagogical purposes. It makes simple the explanation of the principle of operation of a class of AC machines reputed by the complexity of their magnetic circuits. The latter incorporates axially stacked decoupled sub-circuits, one per phase generating alternating magnetic fields. More specifically, there is common air gap, shared by the machine phases, in which a rotating magnetic field is created by the superposition of the PM contribution and the armature one.

Accounting for the complexity of the magnetic circuits and the three-dimensional (3D) flux paths characterizing TFPMs, a 3D finite element analysis (FEA) is required for the validation of the analytical predictions. Nevertheless, such a 3D FEA validation is far from being obvious to be carried on within a master lecture.

While the basis of Lorentz forces resulting from fictitious current loops emulating PMs has been considered in some referenced papers, its simple and pedagogical application to assess the torque production of several TFPM concepts represents the added value of the present paper.

This paper aims to examine how bank stress tests affect bank tax planning.

The study uses US bank stress test bank size thresholds and a regression discontinuity design to investigate the effect of the Dodd-Frank Act and the instituted bank stress tests on bank tax planning. We use different measures of tax planning, including bank-specific measures and measures of tax avoidance, tax aggressiveness, and effective tax planning from recent literature. Our regression discontinuity and difference-in-differences regression analyses include bank and year fixed-effects and lagged bank characteristics to control for potential endogeneity.

This study finds that stress tests have the unintended consequences of intensifying tax planning and increasing tax avoidance. Stress-test banks increase tax avoidance by accelerating charge-offs, net interest, and non-interest expenses. However, this increase in tax planning is not optimally maximized, leading to lower effective tax planning compared to non-stress-test banks. Banks with a substantial increase in tax avoidance under the Dodd–Frank Act tend to increase their risk, investing in high-risk-weight assets and lending in riskier loan categories. These findings are consistent with tax minimization conditions under added regulatory attention and policy uncertainty.

Literature on bank tax planning is limited. Most tax avoidance literature excludes financial institutions such as bank holding companies mainly due to differences in business practices and regulatory frameworks. This study is the first to investigate tax planning behavior among US banks. The current study thus extends the research field by examining the effect of bank transparency regulations, such as bank stress tests, on bank tax planning activities. Our findings have a direct bank policy implication. They show that stress testing has the unintended consequences of increasing tax planning activities and consequently increasing risk-taking on banks with high tax avoidance, which goes against the goals of stress testing regulations.

Generative Artificial Intelligence (Gen-AI) has provided new opportunities and challenges in using educational environments for students’ interaction and knowledge acquisition. Based on the expectation–confirmation theory, this paper aims to investigate the effect of different constructs associated with Gen-AI on engagement, satisfaction and word-of-mouth.

We collected data from 508 students in the UK using Qualtrics, a prominent online data collection platform. The conceptual framework was analysed through structural equation modelling.

The findings show that Gen-AI expectation formation and Gen-AI quality help to boost Gen-AI engagement. Further, we found that active engagement positively affects Gen-AI satisfaction and positive word of mouth. The mediating role of Gen-AI expectation confirmation between engagement and the two outcomes, satisfaction and positive word of mouth, was also confirmed. The moderating role of cognitive processing in the relationship between Gen-AI quality and engagement was found.

This paper extends the Expectation-Confirmation Theory on how Gen-AI can enhance students’ engagement and satisfaction. Suggestions for future research are derived to advance beyond the confines of the current study and to capture the development in the use of AI in education.