The rapid advancement of Generative Artificial Intelligence (GAI) has created new opportunities and challenges for engineering education, particularly in Mechanical Engineering Education (MEE). This study investigates the adoption and use of GAI in MEE. Drawing on a literature review and semi-structured interviews with faculty members and students, key enablers, challenges, strategies, and psychological factors influencing GAI adoption were identified. Survey data were subsequently analysed using the Relative Importance Index (RII) to prioritize the factors perceived as most influential by both stakeholder groups. The findings indicate that strategy-related factors, particularly institutional governance and clear policies for GAI use, were prioritized by both faculty members and students. Pedagogical and ethical considerations were perceived as more influential than technical barriers. Furthermore, students perceived GAI to be most valuable for technically intensive tasks, including design, simulation, and coding, whereas collaborative applications received the lowest priority. Overall, the findings provide evidence-based insights into the factors influencing GAI adoption and the areas in which students perceive the greatest value for its application, supporting the structured and pedagogically aligned integration of GAI within Mechanical Engineering Education.

Following the unprecedented military attacks on Kuwait that have started in February 2026, the educational sector, along with other systems, have been compelled to implement swift emergency measures to maintain continuity. As a result, classes and examinations have transitioned to an online format. Throughout this challenging period, students have adapted by utilizing advanced Generative Artificial Intelligence (GenAI) tools to complete their coursework. This adaptation not only demonstrated the education system’s resilience but also underscored the evolving role of technology in academic processes. This study explores the adoption of GenAI tools among students at Kuwait University through an extended Technology Acceptance Model (TAM) that incorporates ethical factors. A cross-sectional online survey was conducted, gathering 390 responses with majority from undergraduate students in their first two years across various colleges. Quantitative analyses have confirmed the core TAM relationships, showing that both perceived ease of use and perceived usefulness positively influenced students’ attitudes toward using GenAI tools. These attitudes, in turn, shaped behavioral intentions and ultimately led to actual use of the tools. Further mediation analysis revealed that students’ personal norms fully mediate the relationship between ethical awareness of consequences and attitudes toward using GenAI tools. Most importantly, ethical awareness affects the adoption of GenAI tools indirectly via personal norms. The implications of this study contribute to both theoretical understanding and practical application of AI adoption in higher education, particularly during periods of crisis. The findings offer valuable insights for policymakers and academic lecturers aiming to integrate GenAI tools into learning environments.

Higher education institutions face urgent decisions about integrating generative AI tools such as ChatGPT into Computer Science (CS) curricula, yet most policy responses remain reactive rather than evidence-based. Applying the Unified Theory of Acceptance and Use of Technology (UTAUT), this study tests whether Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) predict CS students' Behavioural Intention (BI) to use ChatGPT as a learning copilot. A cross-sectional survey of undergraduates in CS, Cyber Security, Data Science, and Software Engineering programs (N = 325) was analysed using PLS-SEM with bootstrapping. The model explained 84.2% of variance in BI. PE was the dominant predictor (β = 0.627, p < .001) and FC a meaningful secondary driver (β = 0.324, p < .001); EE and SI were non-significant in this post-adoption sample. Findings inform a four-pillar policy framework for sustainable institutional AI integration in CS programs.

The labour market of the United Arab Emirates (UAE) presents a unique case study characterized by a significant expatriate majority and a deliberate national policy of Emiratisation. This article examines the historical evolution, contemporary dynamics, and socio-economic impacts of Emiratisation, a strategic imperative aimed at increasing participation of UAE nationals in the private sector who have mostly been drawn to public sector employment. The article further explores the regulatory framework, key initiatives like NAFIS, and the implications of compliance issues. The article concludes with insights into how to cultivate a competitive, innovative, and sustainable national workforce.

Supply chain planning coordinates demand, sourcing, inventory, capacity, logistics, and disruption response across complex supply networks. Sustainability pressures have expanded planning beyond cost and service performance to include environmental performance, resilience, social and ethical responsibility, transparency, and accountability. At the same time, organizations are exploring Generative Artificial Intelligence (GenAI) to support scenario generation, knowledge synthesis, decision explanation, and natural-language interaction with planning systems. Drawing on the Resource-Based View (RBV), this paper examines how GenAI technological resources and associated organizational capabilities can contribute to competitive advantage in sustainable supply chain planning. The study analyzes five resources: large language models, retrieval-augmented generation, GenAI-enabled optimization and decision analytics, multi-agent GenAI systems, and knowledge graphs. It also examines six capabilities, including GenAI proficiency, data governance, sustainability-aware planning, ethical oversight, human-AI collaboration, and cross-functional learning. The analysis argues that GenAI creates value when embedded within firm-specific knowledge, data, governance routines, and human expertise.