Mohamed Benaida

As governments expand artificial intelligence (AI) in public services, user trust may depend on the governance safeguards surrounding its deployment. This exploratory study combines a documentary analysis of 16 AI governance dimensions in the United Kingdom and Saudi Arabia with a survey of 100 GOV.UK and Absher users recruited through non-probability sampling (50 per group). The documentary comparison identifies contrasting governance arrangements, with a more distributed, sector-led approach in the United Kingdom and more centralised coordination in Saudi Arabia. Using an adapted 12-item trust questionnaire, surveyed Absher users reported higher average scores than surveyed GOV.UK users (3.47 versus 3.29), with the largest item-level differences concerning suspiciousness, wariness, and perceived integrity. Given limited internal consistency and unestablished English–Arabic measurement equivalence, these differences are interpreted as exploratory item-score patterns. Respondents’ awareness of national AI governance arrangements was not assessed, so structural assurance is discussed as a theoretical lens rather than a tested explanation. The study contributes a structured governance comparison, descriptive evidence of trust-item patterns in two selected platform-user groups, and a five-component framework addressing transparency, accessibility, accountability, enforcement visibility, and continuous trust monitoring.

As governments expand artificial intelligence (AI) in public services, user trust may depend on the governance safeguards surrounding its deployment. This study examines the association between national AI governance arrangements and user trust in e-government platforms in the United Kingdom and Saudi Arabia. A comparative mixed-methods design combined documentary analysis across 16 governance dimensions with a cross-sectional survey of 100 GOV.UK and Absher users (50 per platform). Trust was measured using an adapted 12-item trust-in-automation checklist. Absher users reported higher overall trust than GOV.UK users, t(98) = 3.30, p = .001, Cohen’s d = .66 (95% CI [.26, 1.06]). The difference was concentrated in the study-defined integrity-related dimension, while the ability-related composite did not differ significantly. A direct integrity-minus-ability contrast confirmed that the between-group difference was greater for integrity-related perceptions, t(98) = 5.27, p < .001, d = 1.05. Although the United Kingdom ranked higher on the 2025 Government AI Readiness Index, this was not accompanied by higher user trust in the platform examined. The findings are consistent with structural assurance as a possible interpretive mechanism, while not establishing causation. These findings should be interpreted in light of the modest sample size and adapted measurement approach. The study proposes a five-component framework centred on binding transparency, accessibility compliance, clearly assigned accountability, enforcement visibility, and continuous trust monitoring, and provides an initial cross-national benchmark for examining user trust under contrasting AI governance environments.

Extreme heat poses a critical risk in high-density mass gatherings, where atmospheric conditions, urban morphology, and crowd dynamics interact to produce hazardous microclimates. During the Hajj pilgrimage in Makkah, these effects are amplified by dense crowds, heat-retaining surfaces, and constrained ventilation. Conventional Urban Heat Island (UHI) indicators based on land surface temperature do not directly capture human thermophysiological stress and are therefore inadequate for operational heat-risk assessment. This paper introduces a physics-guided hybrid framework for modeling crowd-modified wet-bulb heat risk. The approach combines (i) wet-bulb temperature as a physiologically grounded baseline; (ii) a data-driven spatial amplification term derived from satellite-based morphological features; (iii) scenario-based estimates of crowd-induced heat contributions; and (iv) wind-related modulation that accounts for stagnation and dust effects. By explicitly separating time-varying atmospheric forcing from spatial susceptibility, the framework provides an interpretable decomposition of heat-risk drivers. The machine-learning model for estimating the spatial amplification component achieves a mean absolute error of 1.13 °C (R² = 0.586). Application of the framework to the 2021–2025 Hajj seasons suggests distinct site-specific heat-risk regimes, with Arafat and Muzdalifah dominated by urban amplification and Haram and Mina more strongly influenced by crowd density. Across all years, the combined influence of urban morphology, crowd density, and wind conditions results in an average increase of approximately 2.1 °C to 2.3 °C in effective wet-bulb temperature across sites. The 2024 Hajj season is identified as the most severe, with a substantial increase in extreme-risk exposure. The proposed framework provides a spatially explicit, physically interpretable, and decision-support-oriented methodology for heat-risk assessment within the Hajj context. The decomposition-based framework is, in principle, transferable to other large-scale mass-gathering environments, subject to retraining of the spatial amplification model and replacement of the event-specific crowd allocation component with context-appropriate crowd estimation approaches.

As digital technologies and the Internet evolve, digital issues are becoming more prevalent and are a serious concern due to their increased level of integration in our daily lives. Many users lack the necessary digital literacy to protect themselves and their communities from such issues. Background research reveals that adolescents in Saudi Arabia are particularly vulnerable to these issues, also supported by the studies pre-test for students (15.8%). This highlights a gap regarding preventative measures that researchers aim to fill. This study investigates the effectiveness of integrating a dedicated mobile application focused on raising awareness regarding digital issues such as cyberbullying within a hybrid teaching framework amongst primary and middle school students in Saudi Arabia. The application’s features, including but not limited to pre-tests, post-tests, lecture material, and progress monitoring, along with its ease of use, are designed to efficiently achieve digital issue learning outcomes. To assess this hybrid approach toward raising awareness of digital issues, this study gathers 30 participants and splits them into three groups: group one (hybrid), group two (traditional), and group three (online only). The results, which were analysed via one-way analysis of variance (ANOVA) and Tukey’s honest significant difference (HSD) post-hoc test, demonstrate a significant advantage in the increased level of awareness of students from the first group. This indicates that our mobile application in hybrid settings is significantly more effective in enhancing knowledge retention amongst students and raising their level of awareness of digital issues.

Universities have focused on learning outcomes as a metric for evaluating the quality assurance of educational systems. However, due to the absence of a clear guide on measuring outcomes and analysing them accordingly to improve the educational cycle, educators struggled to efficiently and effectively address shortcomings. By proposing a new framework, this study fills this gap and provides a guided approach to measuring learning outcomes in the context of university e-learning systems. Facilitated by a comprehensive research literature review of 102 articles, filtered from 271 articles using the PRISMA method, the opinions of five experts regarding e-learning systems and their outcomes were analysed using the analytical hierarchy process (AHP) to provide priority rankings. Teaching methods, teaching quality, learning environment, and students emerged as the main factors, along with their sub-factors. Moreover, our experts from diverse educational and geographical backgrounds provide added value, enabling the framework to be implemented across various environments and fields. Ultimately, this framework accurately measures sub-factors to identify the strengths and weaknesses of educational variables. The proposed framework is a step in the right direction, enabling the design of a suitable system that takes into account users’ needs when assessing e-learning outcomes.

E-governments are considered the primary gateway through which citizens can access services and benefit from the information provided by electronic platforms when needed. Although the situation in developed countries has shifted from electronic governments to local electronic services, many developing countries still encounter various challenges, such as ease of use and enhancing user experience. This study aims to help address the obstacles faced by North African countries, including Morocco and Tunisia, in the context of e-government usability. It evaluates the accessibility of these sites, addresses the issues involved in using them, and suggests solutions that could enhance user experience by improving ease of use. The limited previous research on this region makes this study even more relevant. Research-based web design and usability guidelines (RBWDUG), specifically the chapter on optimizing user experience, serve as the foundation of this research, facilitating the assessment of ease of use and user satisfaction. Converting this valuable tool into a questionnaire enabled the research to achieve valuable results through evaluation. The evaluation of ten websites in each country, with 100 participants involved in the assessment, reveals a common pitfall when it in improving user experience and ease of use. This issue impacts user satisfaction and the effectiveness of e-government services. An important relationship has been established between these usability issues and the effectiveness and efficiency of these services. The study emphasizes the vital connection between usability and e-government service efficiency while recommending improvements based on user feedback.

The COVID-19 is an exponentially growing disease that has intentioned nations to use technologies to detect the coronavirus infection. Several nations are working greatly to fight against COVID-19. Many nations have been using a range of devices to combat the pandemic, seeking information about growth, monitoring as well as the leaking the confidential information of the residents. This research aims to assist infected people online using the Internet of Things (IoT) and Blockchain technologies through smart devices. IoT-based healthcare devices gather useful information, provide additional insight through symptoms and behaviors, allow remote monitoring, and simply give people better self - determination and healthcare. Blockchain allows the secure transfer of patient health information, regulates the medical distribution network. A four-layer architecture is proposed using IoT and Blockchain to detect and prevent individuals to be COVID 19. This research provides a framework for patients with COVID-19 infectious disease and recognizes health issues and diagnoses online. Smart devices such as smartphones can install any mobile apps such as Aarogya Setu, Tawakkalna, and so on. These applications can track COVID-19 patients properly. The installation of mobile apps on smart devices focuses to reduce the time and cost and increase the performance of the infectious patient's condition. A four-layer architecture is proposed using IoT and Blockchain technologies. Many research works focus on investigating, analyzing, and highlighting the affected individuals through guiding the COVID-19 infection. Eventually, various mobile apps are recognized and addressed in this paper.

The COVID-19 is a highly contagious infection which is already had a catastrophic effect on the most serious diseases, including death, around the world. Blockchain-based healthcare systems are being introduced to help simplify mobile and telehealth services, reducing patient stress and the cost of critical clinical services. shared the advantages of blockchain for building a cutting-edge authentication infrastructure and detecting COVID-19 suspicious cases. The authors presented a blockchain-based design for developing a real-time cellular health monitoring system for COVID-19 patients in this paper. This study identifies clinical problems and electronic diagnoses for people with COVID-19 infectious diseases and provides a framework for them. Any mobile application can be configured on digital devices such as smartphones. COVID-19 patients may benefit from such applications. Smartphone apps are designed to save time and money while increasing the efficiency of infectious patients. IoT and Blockchain strategies are presented in the four-layer structure.