Search Results for smart-university
Abstract
The rapid advancement of digital technologies has transformed the global higher education landscape, making the transition toward smart universities a strategic necessity rather than a developmental option. Despite ongoing reform efforts in Iraq, higher education institutions continue to face significant challenges related to governance, digital infrastructure, data management, research capacity, and institutional readiness. The research problem stems from the existence of a substantial gap between the current state of Iraqi universities and the requirements of internationally recognized smart university models, which limits their ability to contribute effectively to sustainable development and the knowledge economy. This study aims to analyze the current status of smart university transformation in Iraq through a gap analysis approach and to develop a strategic roadmap that supports the transition process. Using a qualitative analytical methodology, the study examines the Iraqi higher education environment, identifies major transformation gaps, and evaluates key dimensions including governance, digital infrastructure, policy frameworks, pedagogy, research and innovation, stakeholder engagement, financing, and risk management. The findings reveal significant deficiencies in digital readiness, institutional integration, data governance, technological infrastructure, and university–industry collaboration. The study highlights the importance of establishing a coherent national framework that aligns digital transformation initiatives with higher education policies and development priorities. Based on the identified gaps, a comprehensive strategic roadmap is proposed to guide universities and policymakers through a phased transformation process toward smart university implementation. The study contributes to the growing body of knowledge on higher education digital transformation by providing a context-specific framework for Iraq and offering practical policy recommendations to enhance institutional performance, innovation capacity, educational quality, and long-term socio-economic development.
Abstract
This study aims to analyze the role of reengineering digital thinking among university leaders and their belief in digital transformation in creating and adopting the smart university, which keeps pace with developments in artificial intelligence and digital tools. These represent some of the most important dimensions of digital transformation in an era in which digitalization has become one of the most significant competitive priorities. University leaders should therefore incorporate digital readiness into their strategic plans to ensure that the university completes the requirements of digital transformation, reaches the stage of becoming a smart university, and moves beyond traditional practices. Given the importance of reengineering leadership mindsets to foster belief in digitalization within higher education institutions and the need for universities to keep pace with rapid technological changes, the study problem was formulated around the question of the extent to which reengineering digital thinking among university leaders contributes to enhancing universities' digital readiness in a manner that supports the adoption of smart university dimensions and the achievement of the requirements of modern university transformation. The study adopted a descriptive-analytical approach. Its population and sample consisted of university leaders, including college deans, assistant deans, and heads of departments. A total of 73 valid responses were obtained for analysis. The study was based on three main variables. The first variable was reengineering digital thinking, comprising the following dimensions: leadership's belief in digital transformation, strategic digital vision, data-driven thinking, rapid responsiveness to digital changes, digital knowledge sharing, and digital skills updating. The second variable was digital readiness, comprising the dimensions of digital foresight, agile organizational structure, digital governance, digital competencies, and digital proactivity. The third variable was the smart university, comprising the dimensions of smart infrastructure, smart education, smart digital administration, smart services, digital innovation, and smart core capabilities of faculty members. The study found a significant correlation and significant effect between reengineering digital thinking and digital readiness. It also revealed a significant effect of digital readiness on the adoption of the smart university. These findings indicate that developing and reengineering digital thinking among university leaders contributes effectively to enhancing the level of digital readiness of universities, which, in turn, positively affects their ability to adopt smart university systems and improve the efficiency of academic and educational performance. The results also demonstrated that strategic digital vision, data-driven thinking, and digital skills updating are among the most influential dimensions in supporting the transition toward the smart university. The study recommends that universities adopt specialized programs for reengineering the digital thinking of university leaders, strengthen digital governance, develop smart infrastructure, invest in developing digital competencies, and foster a culture of digital innovation. Such initiatives would contribute to the transition toward smart universities and enhance their competitiveness and sustainability in the contemporary higher education environment.
Abstract
Managing network infrastructure resilience under hostile conditions is a fundamental priority for modern smart university ecosystems. This paper introduces an advanced analytical framework to model and quantify the resilience of hybrid networks protected by Shamir Secret Sharing and Diffie-Hellman cryptographic protocols under intelligent targeted cyber-attacks. The network architecture incorporates a primary hybrid path operating under a 4-out-of-5 threshold logic alongside an independent fallback path managed by a backup componentB_6. A rigorous mathematical derivation using probability theory establishes the joint availability equations for both normal operation and targeted strategic strikes, where an attacker specifically aims to compromise vital edge components B_1andB_5. To validate the closed-form reliability expressions, a robust Monte Carlo simulation with 10^5 iterations was executed in MATLAB. The numerical findings demonstrate that while targeted attacks reduce the primary path's capacity to zero, the independent backup mechanism successfully preserves data-flow continuity and encryption integrity, maintaining a steady system availability floor of R_(sys.)≥ 0.90. These insights provide concrete engineering design criteria for implementing fault-tolerant secure data pathways in digital academic institutions.
Abstract
The rapid shift towards artificial intelligence and its adoption in educational institutions to manage their educational and administrative affairs has produced large amounts of digital data. The research aims to design an integrated governance framework to protect the privacy of student data, making the commitment to protection a competitive advantage not a functional burden. The research used a comparative analytical approach to analyze institutional reports and documents. Among its findings was that the absence of governance in a smart university transforms data from a strategic asset into a source of risk that threatens the university's reputation. This research presents a model based on three main dimensions P3: political, people, and Platform. This model is applicable to universities that rely on artificial intelligence, unlike previous studies that focused on these dimensions separately to address the problem of student data protection. The shortcoming of those studies lies in the absence of a clear model for the interaction of these three dimensions.
Abstract
Smart universities constitute a complex environment in which thousands of Internet of Things (IoT) devices are interconnected, rendering them an increasingly attractive target for sophisticated cyberattacks. This study aims to design an integrated framework combining Artificial Intelligence (AI) and IoT technologies for the early detection of cyberattacks within smart university environments, employing machine learning and deep learning algorithms — namely Random Forest and a hybrid CNN-LSTM model — to classify network traffic and detect anomalous patterns in real time. The study adopted a descriptive-analytical approach supported by a field questionnaire administered to a sample of 150 participants from IT and network security personnel across universities. Results revealed excellent questionnaire reliability (Cronbach's Alpha = 0.927) and a strong positive correlation (r = 0.674) between cybersecurity awareness and AI detection effectiveness, alongside the superiority of the proposed model over traditional systems by an accuracy margin of approximately 25 percentage points (96.3% vs. 71.2%). The study recommends adopting a five-layer architectural framework integrating IoT devices, data processing, AI inference, attack detection, and real-time response layers as a viable model for deployment in Arab smart universities.