As inculcating green intention and behaviour among individuals, particularly potential entrepreneurs (students), has recently become a hot topic of discussion, it is essential to contribute to this debate by offering empirical evidence. Accordingly, the objective of this study is to examine how green mindfulness (GM) and spiritual intelligence (SPI) enhance environmental self-identity (ESI). This study also examined the role of GM and SPI in developing green entrepreneurial intention (GEI). The study investigated whether ESI could mediate the relationship between GM, SPI, and GEI. The study gathered information from 202 students at King Faisal University’s Business Administration and Applied Colleges in Saudi Arabia using a self-administered questionnaire for these objectives. The study employed partial least-squares structural equation modeling to analyse the data. The study revealed some interesting findings. It was discovered that GM has a direct and significant positive association with ESI and GEI. Furthermore, it was also reported that SPI has a positive and significant connection with ESI, but no positive relationship with GEI. ESI revealed the existence of a meaningful positive relationship with GEI. Finally, when examining the mediating role played by the ESI, the study reported the inability of the ESI to mediate the relationship between GM and GEI. At the same time, it was able to fully mediate between SPI and GEI. The study provides a set of recommendations for policymakers and other stakeholders. The study is considered one of few studies investigating the key factors influencing GEI among students.

This chapter examines AI governance in Saudi higher education through a Triple Helix model of university–government–industry integration, grounded in Saudi Vision 2030 and the National Strategy for Data and Artificial Intelligence (NSDAI). It analyzes how national strategy, regulatory requirements for data protection and cybersecurity, and institutional governance practices shape AI adoption across universities. Building on helix-based innovation theory and qualitative policy analysis, the chapter proposes a Strategic Governance Framework for Artificial Intelligence (SGF-AI) that integrates strategic alignment, ethical and legal oversight, operational coordination, and multidimensional impact evaluation. The framework is illustrated through a case study from the Applied College at King Faisal University and extended with Quadruple and Quintuple Helix perspectives to incorporate societal engagement and sustainability, concluding with a phased roadmap for 2025–2035.

Universities worldwide face escalating financial pressures while simultaneously confronting demands to adopt sustainable practices. This study examines whether Sustainable Green Innovation (SGI) serves as the central transmission mechanism through which AI-Driven Entrepreneurial Orientation (AI-DEO) and Green Metrics for Self-Efficacy (GMSE) influence University Financial Sustainability (UFS) in higher education institutions, within the broader framework of Circular Economy Principles (CEP). Cross-sectional survey data were collected from 1279 faculty and staff members at King Faisal University (Saudi Arabia) and Benha University (Egypt) between October 2025 and January 2026. Regression-based structural path analysis with nonparametric bootstrapped mediation testing (5000 resamples) was employed to test four hypotheses. The results show that AI-DEO emerged as the dominant positive predictor of SGI (β = 0.732, p < 0.001), while GMSE did not show a statistically significant association with SGI once AI-DEO was included in the model (β = −0.034, p = 0.246). SGI emerged as the strongest predictor of UFS (β = 0.369, p < 0.001), supporting an innovation-mediated route to financial sustainability. The indirect effect of CEP on SGI through AI-DEO was statistically significant (indirect = 0.3473; 95% CI [0.3123, 0.3835]), supporting full mediation. Hypotheses H1, H2a, H2b, and H3 were supported; H4 was not supported. These findings indicate that financial resilience in universities is primarily innovation-mediated and orientation-driven. The study contributes to the sustainability-in-higher-education literature by clarifying SGI as a central transmission mechanism and highlighting the importance of building operational green competence alongside strategic AI-Driven entrepreneurial orientation.

Universities rely on digital arrangements that distribute credential verification, model development, and educational interaction across networks and platforms, while responsibility for academic outcomes remains institutional. This chapter introduces Strategic Governance for Decentralized Classrooms (SGDC) to explain this control–responsibility gap. Drawing on an integrative synthesis of governance, leadership, and emerging-technology research, the framework distinguishes three operational capabilities: distributed trust, distributed educational intelligence, and distributed educational presence. Strategic leadership aligns them into an integrated governance capability, while institutional legitimacy mediates the relationship between this capability and educational and public-value outcomes. Five propositions specify the relationships and boundary conditions. SGDC supports decentralization only when universities preserve educational purpose, meaningful decision rights, safeguards, adaptive oversight, and effective redress.

Students with disabilities find learning challenging in traditional learning environments due to the lack of instructional strategies that are not personalized or adaptive. Various studies have focused on identifying learning difficulties such as dyslexia, dysgraphia, and dyscalculia, which require a multi-step screening process under the supervision of psychologists. Identifying these difficulties is challenging but essential, as it impacts a student's learning and academic success. Everyone's comprehension ability depends on several factors, including the experience and knowledge they bring to the learning environment. With the evolution of technology and the advancement of e-learning platforms, adaptive e-learning has bridged the gap between students' needs and educational institutions' extra classes, enabling students to select targeted courses aligned with their interests. Since the onset of COVID-19, universities have recognized the necessity of online learning and have continued to use these platforms for student assessment. Educational institutions seek innovative strategies to enhance personalized learning (PL) for students with disabilities. The use of technology in schools has created new opportunities for PL, especially for students with disabilities. Traditional learning systems frequently neglect the diverse focuses and distinct requirements of students with visual, cognitive, motor, or auditory impairments. Because of this lack of flexibility, students with disabilities are less likely to be engaged and to complete tasks. Identifying and using effective learning strategies that work for each student remains a significant challenge in education. To address this issue, this paper proposes a reinforcement learning–based PL system for students with disabilities (PLS-SD) using Q-learning. We suggest PL actions, including audio instructions, augmented reality, and text-based resources. A reward system based on real-world outcomes, i.e., how well students complete their work and how engaged they are, helps them learn. Experimental results demonstrate that the proposed approach effectively identifies optimal actions for different learner states, with immersive and adaptive strategies, such as augmented reality and interactive content, consistently achieving higher rewards. The model shows stable learning behavior across training episodes and successfully adapts its policy to maximize learner engagement and task completion. As indicated by comparing the results with currently advanced models, the proposed method outperforms approaches that are considered traditional by providing context-aware and adaptable recommendations. These findings highlight the potential of reinforcement learning to support scalable and personalized educational solutions for diverse learners.