Volumes and Issues

 Abstract The application of Information and Communication Technologies (ICTs) in academia is generally classified into three classes: ICTs as supporting tools, ICTs as subjects of study, and ICTs as drivers of transformation. The primary objective of the research was to assess and analyze the state of ICT resources in the Kingdom of Saudi Arabia (KSA) educational institutions. In light of Saudi Arabia’s Vision 2030, which prioritizes digital transformation and the integration of technology into education as a foundation for building a knowledge-based economy. This research aimed to explore the objectives that academia had for incorporating ICTs into their teaching, to examine whether institutes possessed the essential ICT infrastructure to achieve these objectives, and to evaluate whether the actual use of ICTs aligned with these stated objectives. Furthermore, this study also sought to identify any discrepancies between private and government schools in their approach to ICT integration. To gather data, we employed a hybrid approach which involve interviews and surveys distributed digitally via email and messaging platforms. The findings revealed that while intermediate schools and a significant number of secondary schools claimed to support transformative or innovative applications of ICTs, the reality was different. Access to laptops, PCs, peripherals such as printers, scanners, projectors etc., and the Internet connectivity for Saudi students was largely adequate. The availability of software was largely confined to basic productivity tools, limiting the scope of ICT use primarily to equipping students with basic computer operational skills. Although private schools were found to be better equipped than public schools, the overall use of ICTs in education remained similarly constrained across both sectors. The research highlighted a gap between the potential transformative goals that some schools professed and the actual, more limited application of ICTs in practice.

Alaklabi, S. (2025). Aligning ICT Ambitions with Reality: The Impact of Technology on Education in Saudi Arabia. Journal of Shaqra University for Computing and Information Technology, 1(1), 1-12.

Abstract Smartwatches and fitness trackers generate vast amounts of sensitive health data, but traditional machine learning requires centralized collection, raising privacy concerns under HIPAA and GDPR. In this work, we present a privacy-preserving federated learning framework for smart healthcare devices allowing shared training of models with patient privacy protections. Our framework is an Adaptive Differential Privacy Federated Learning (ADP-FL) algorithm, which guarantees privacy protections accounting for the data heterogeneity and maintains clinical utility. The system addresses wearable device constraints including limited computational resources and non-IID data distributions. Evaluation using PhysioNet and MIMIC-III datasets demonstrate 87.3-92.1% accuracy for cardiac arrhythmia detection with differential privacy guarantees (epsilon 1.2-6.8). The system limits membership inference attacks to near-random performance (51.2-53.8%) and maintains communication efficiency at 0.8 MB per device per round with 3.2% battery overhead. Scalability testing with 5,000 devices shows minimal performance degradation, establishing federated learning as viable for collaborative healthcare AI while preserving privacy.

Al Qwaid, M. (2025). ADP-FL: Adaptive Differential Privacy Federated Learning for Secure and Scalable Smart Healthcare. Journal of Shaqra University for Computing and Information Technology, 1(1), 13–21.

Abstract This study explores Genetic Algorithms (GAs) in depth. It highlights their growing impact as powerful optimization tools in various scientific domains. Emphasis is placed on their application in resolving Bluetooth channel interference, an increasingly critical issue due to the rapid proliferation of wireless devices. Inspired by the principles of natural evolution, the pro-posed GA approach optimizes channel allocation by iteratively refining solutions through selection, crossover, and mutation operations. The experimental evaluation reveals notable improvements in network performance, including reduced channel interference, lower packet loss, and enhanced energy efficiency. In addition to the practical contributions, this paper provides a comprehensive review of GA design principles, advantages, limitations, and emerging research directions. The findings demonstrate the potential of GAs in delivering scalable, adaptive solutions for dynamic spectrum management in modern wireless communication systems.

Alhassan, A., Altmami, N., Mujahed Alanazi, A., & Alghamdi, A. (2025). Adaptive Genetic Algorithm for managing signal interference in Bluetooth Network. Journal of Shaqra University for Computing and Information Technology, 1(1), 22–29.

Abstract Accurately predicting student performance has become a priority in the field of educational data mining, offering valuable insights for early intervention and academic planning. This study presents a hybrid approach combining machine learning and metaheuristic algorithms for enhanced predictive accuracy. The XGBoost regression model is optimized using three feature selection techniques: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). Experimental results show that PSO consistently outperforms other algorithms in reducing prediction error. The proposed framework highlights the importance of intelligent feature selection in improving academic prediction systems.

Altmami, N., Alhassan, A., Almutairi, M., & Almaaz, H. (2025). Predicting Student Performance using Metaheuristic Optimization and XGBoost. Journal of Shaqra University for Computing and Information Technology, 1(1), 30–37.

Abstract This study examines the impact of artificial intelligence (AI) and robotics on productivity, employment, and inequality, integrating data from the International Federation of Robotics (IFR) and the World Bank’s World Development Indicators (WDI) for the period 2000–2022. While robotics adoption has rapidly increased across the world, the economic and social impact is still a disputed matter. Using a panel data analysis with country and year fixed effects, the study shows that a higher robot density is significantly related to productivity increases, validating the view of AI and robotics as general-purpose technologies that improve productivity and output. However, results also show labor market and distributional impacts that are non-uniform. The robot density and job indicator have a slight negative correlation, indicating that automation is replacing traditional labor-intensive work in emerging economies. In contrast, developed economies are better equipped to absorb the displacement through reallocation and reskilling. In addition, we find that there is a strong positive correlation between robot density and income inequality, with greater adoption being associated with increased wage polarization. These results highlight the dual nature of automation: it serves as an engine of economic growth while also intensifying societal risks. The paper concludes that policy frameworks play an important role in determining these outcomes. Improving social protection systems, enhancing labor market institutions, facilitating inclusive innovation policies, and increasing investment in human capital are necessary to reap the benefits from productivity improvements, while reducing negative implications for workers. If we don't have carefully coordinated national and international strategies, the benefits of adopting robots will be unevenly distributed, which will increase inequality and ultimately destroy long-term social cohesion.

Alotaibi, M. (2025). Artificial intelligence and robotics transforming productivity growth, labor markets, and income distribution. Journal of Shaqra University for Computing and Information Technology, 1(1), 38–47.