Saudi Cultural Missions Theses & Dissertations
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Item Restricted Leveraging AI in Saudi Medical Tourism(2025) Aljohani, Elaf Maher S; Dr.Tiia MäkinenThis research examines the integration of artificial intelligence (AI) in Saudi Arabia’s medical tourism sector, highlighting its potential to enhance healthcare services, operational efficiency, and foreign investment. Using qualitative methods, including expert interviews and global case studies, the study analyzes AI-driven innovations in leading countries and Saudi Arabia’s readiness for adoption. Findings reveal that AI improves diagnostics, patient care, and resource management but faces regulatory and infrastructural challenges. Addressing these barriers requires policy frameworks, investment in AI infrastructure, and collaboration between healthcare providers and technology firms. The study contributes to understanding AI’s role in medical tourism and its alignment with Saudi Vision 2030.6 0Item Restricted Employee Readiness for AI Adoption in Riyadh’s Healthcare Sector: Perceptions and Organizational Support(Saudi Digital Library, 2025) Almutairi, Hadeel; Cui, QinquanArtificial intelligence (AI) is widely recognized as a significant driver of digital transformation across several domains, with the healthcare sector identified as one of the most influenced sectors. This research assesses employee readiness for AI among healthcare professionals in Riyadh, Saudi Arabia, with particular attention paid to perceptions (perceived usefulness and ease of use) and organizational support, including training and management support. This study employed a quantitative, cross-sectional, and correlational design. A survey was administered to evaluate employee readiness levels and potential predictors of AI readiness. A total of 120 employees participated with overall readiness (M = 4.20, Var=0.64). The regression explained 39.4% of the variance in readiness, with perceived usefulness (B = 0.44, p < 0.001) and training (B = 0.40, p < 0.001) contributing positively to readiness, while management support contributed negatively (B = -0.17, p = 0.011), and ease of use was not significant (B = 0.05, p = 0.574). Independent t-tests and ANOVA confirmed no significant differences in readiness by gender (p = 0.40), job type (p = 0.44), or years of experience (p = 0.56). The results showed that perceived usefulness and training were the strongest predictors of employee readiness for AI. While ease of use was not significant, organizational support had a negative effect. This study contributes to the literature on AI readiness in Saudi healthcare, highlighting perceived usefulness and training as key drivers for AI adoption, while questioning assumptions about the management support role in AI adoption. Healthcare leaders and policymakers should prioritize training, communicate the practical benefits of AI, and ensure that managerial commitment is supported by resources.14 0Item Restricted Evaluating the Effectiveness of Existing AI Models in Energy Management for Smart Facilities and Buildings(Saudi Digital Library, 2025) Aldawsari, Abdulrahman; Morgan, PeterThis project evaluates the practical effectiveness of existing artificial intelligence (AI) models used in energy management systems for smart buildings and microgrids. While the academic literature is rich in high-performing algorithms, little is known about how these models function under real-world constraints such as data availability, system integration, and operator interpretability. The research focuses on four main AI model types: deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU); tree-based models including Random Forest (RF) and Gradient Boosted Trees (GBT); hybrid models combining convolutional neural networks (CNN) and support vector regression (SVR); and reinforcement learning approaches, particularly Proximal Policy Optimisation (PPO). A structured evaluation framework was developed using three pillars: technical performance, operational feasibility, and deployment readiness. Each model was assessed using peer-reviewed results and case studies, with comparative analysis across forecasting accuracy, training demands, interpretability, and integration ease. The findings revealed that deep learning models, particularly LSTM and GRU, excelled in forecasting accuracy but were resource-intensive and opaque to non-specialist users. Tree-based models such as RF offered greater transparency and were easier to deploy but had lower accuracy in complex, time-dependent scenarios. Hybrid models demonstrated the highest accuracy but required significant tuning and maintenance. PPO-based models were effective in dynamic systems like microgrids but presented challenges with explainability and reward design. Federated learning approaches showed promise in decentralised or privacy-sensitive environments, although the results were mixed and highly context-dependent. Key deployment barriers include data quality gaps, limited technical expertise, and poor interoperability with legacy building management systems. Case studies reinforce the view that no model is universally optimal; effectiveness depends on how well a model aligns with the operational environment. For example, interpretable models may be more suitable in public-sector buildings, while advanced reinforcement learning may be better suited to complex, high-investment infrastructure. The study concludes that successful adoption of AI in energy management requires more than technical optimisation. It demands models that are accurate, explainable, and compatible with the real conditions of the buildings they serve. Recommendations include selecting models based on a balance of accuracy and interpretability, planning for model retraining, addressing integration barriers early, and investing in region-specific validation to ensure broader applicability.7 0Item Restricted LingoAid – AI Based Language Aid for Migrants and Refugees(Saudi Digital Library, 2025) Alsuwayyid, Faisal; Angie, WorwoodThis report explores LingoAid, an AI-powered, trauma-informed language learning platform created to address the linguistic barriers that hinder migrants and refugees from accessing essential services and integrating into host societies. With over 123 million people displaced globally in 2024, including 42 million refugees, language proficiency has become a critical determinant of social inclusion, employment, education, and healthcare outcomes. Existing language learning technologies, such as Duolingo and Mondly, are not designed for displaced populations as they assume stable internet connectivity, literacy, and safe learning environments. LingoAid responds to this gap by offering affordable, adaptive, and contextually relevant language learning through artificial intelligence (AI), natural language processing (NLP), and mobile innovation. The platform’s key features—trauma-informed content, offline accessibility, speech recognition, and culturally contextualised learning—provide a practical and inclusive educational experience tailored to real-life scenarios. The business model employs a hybrid structure combining freemium access, institutional subscriptions, sponsorships, and grants to ensure accessibility and financial sustainability. Initial funding of £150,000–200,000 will support product development and NGO-based pilots, with breakeven anticipated by Year 3. By Year 5, LingoAid aims to reach 4–5 million users, generating annual revenues exceeding £3 million and achieving EBITDA margins of 20–25%. This project demonstrates the potential for socially responsible entrepreneurship to merge humanitarian objectives with technological innovation. By aligning with the United Nations Sustainable Development Goals (SDGs 4 and 10), LingoAid redefines digital language learning as a pathway to dignity, empowerment, and sustainable integration for displaced communities worldwide.23 0Item Restricted LingoAid – AI Based Language Aid for Migrants and Refugees(Saudi Digital Library, 2025) Alsuwayyid, Faisal; Worwood, AngieThis report explores LingoAid, an AI-powered, trauma-informed language learning platform created to address the linguistic barriers that hinder migrants and refugees from accessing essential services and integrating into host societies. With over 123 million people displaced globally in 2024, including 42 million refugees, language proficiency has become a critical determinant of social inclusion, employment, education, and healthcare outcomes. Existing language learning technologies, such as Duolingo and Mondly, are not designed for displaced populations as they assume stable internet connectivity, literacy, and safe learning environments. LingoAid responds to this gap by offering affordable, adaptive, and contextually relevant language learning through artificial intelligence (AI), natural language processing (NLP), and mobile innovation. The platform’s key features—trauma-informed content, offline accessibility, speech recognition, and culturally contextualised learning—provide a practical and inclusive educational experience tailored to real-life scenarios. The business model employs a hybrid structure combining freemium access, institutional subscriptions, sponsorships, and grants to ensure accessibility and financial sustainability. Initial funding of £150,000–200,000 will support product development and NGO-based pilots, with breakeven anticipated by Year 3. By Year 5, LingoAid aims to reach 4–5 million users, generating annual revenues exceeding £3 million and achieving EBITDA margins of 20–25%. This project demonstrates the potential for socially responsible entrepreneurship to merge humanitarian objectives with technological innovation. By aligning with the United Nations Sustainable Development Goals (SDGs 4 and 10), LingoAid redefines digital language learning as a pathway to dignity, empowerment, and sustainable integration for displaced communities worldwide.10 0Item Restricted Adoption of AI Itinerary Planners by Young Adults: A UTAUT Study(Bournemouth University, 2025) Alshehri, Omar; Buhalis, DimitriosThe rapid integration of generative Artificial Intelligence (AI) into the tourism sector has created powerful new tools for travel planning. This study investigates the key determinants influencing the adoption of AI itinerary planners among young adults (aged 18-28), a critical demographic of digital natives. The research aim was to develop and empirically validate an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, integrating the core theory with the constructs of trust, personalisation, and perceived risk. A quantitative, cross-sectional online survey was administered, yielding a final sample of 228 valid responses, which were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that Performance Expectancy is the most powerful predictor of behavioural intention, strongly affirming that perceived utility is the primary driver of adoption. Social Influence and Trust also emerged as significant positive determinants. Crucially, the model demonstrated that Perceived Personalisation is a key antecedent, strongly and positively influencing both Performance Expectancy and Trust. In contrast, Effort Expectancy and Facilitating Conditions were found to be non-significant, suggesting these are baseline expectations rather than drivers for this technologically fluent cohort. While Perceived Risk did not directly deter adoption intention, it significantly eroded user trust. The validated model demonstrated substantial explanatory power, accounting for 76.2% of the variance in behavioural intention. The study concludes that young adults' adoption of AI planners is a pragmatic decision driven by utility, social proof, and a foundation of trust cultivated through a personalised user experience. These findings recommend that industry practitioners focus on enhancing personalisation algorithms and transparency to build trust and leverage social influence in marketing efforts to encourage adoption.34 0Item Restricted Role of Artificial Intelligence in Enhancing Metaverse Gaming Experience and Human Interaction: A Case Study of Roblox's AI Implementation(Tokyo University of Technology, 2025) Alotaibi, Omar; Kameda, HiroyukiArtificial Intelligence (AI) is a tool that is useful for enabling and sustaining the Metaverse gaming experience by infusing virtual reality (VR), augmented reality (AR), extended realities (XR), and blockchain. The current research focused on identifying the impact of AI in leveraging immersive experiences and improving human interaction, which plays a crucial role in Metaverse gaming. A quantitative analysis carried out surveys from 200 randomly sampled respondents involved in Metaverse gaming. Using SPSS 26.0, correlation analysis showed that association between the values of ‘r’ of variables Immersive Gaming Experience (r=0.983**), Deep Learning Collaboration (r=0.957**) and Increased Human Interaction (r=0.979**) are greater than 0.7 depicting strong correlation with Metaverse gaming. Regression analysis further confirmed that the role of AI in enhancing the Metaverse gaming experience and human interaction is significant. With the considerable success of AI in Metaverse, the role of DL algorithms is also groundbreaking in leveraging game balance in multiplayer games, satisfying play-testers and designers who own valuable features, real-time rendering, and multi-user design collaboration.17 0Item Restricted Rasm: Arabic Handwritten Character Recognition: A Data Quality Approach(University of Essex, 2024) Alghamdi, Tawfeeq; Doctor, FaiyazThe problem of AHCR is a challenging one due to the complexities of the Arabic script, and the variability in handwriting (especially for children). In this context, we present ‘Rasm’, a data quality approach that can significantly improve the result of AHCR problem, through a combination of preprocessing, augmentation, and filtering techniques. We use the Hijja dataset, which consists of samples from children from age 7 to age 12, and by applying advanced preprocessing steps and label-specific targeted augmentation, we achieve a significant improvement of a CNN performance from 85% to 96%. The key contribution of this work is to shed light on the importance of data quality for handwriting recognition. Despite the recent advances in deep learning, our result reveals the critical role of data quality in this task. The data-centric approach proposed in this work can be useful for other recognition tasks, and other languages in the future. We believe that this work has an important implication on improving AHCR systems for an educational context, where the variability in handwriting is high. Future work can extend the proposed techniques to other scripts and recognition tasks, to further improve the optical character recognition field.81 0Item Restricted Integrating Digital Technologies with Customer Relationship Management (CRM) to Enhance Customer Satisfaction and Loyalty in Luxury Hotels(Manchester metropolitan university, 2024) Assiri, Tarek; Cosser, GillianThis study investigates the integration of digital technologies—namely Artificial Intelligence (AI), Internet of Things (IoT), and Big Data analytics—into Customer Relationship Management (CRM) systems in luxury hotels. The research evaluates the impact of these technologies on customer satisfaction and loyalty through a quantitative approach, utilizing data from surveys conducted with hotel front-office employees. Findings reveal a varied adoption of digital tools, with IoT significantly enhancing operational efficiency, Big Data analytics improving customer retention strategies, and AI demonstrating underutilization due to staff training challenges. The study underscores the importance of aligning technology adoption with employee proficiency and guest expectations to optimize CRM effectiveness. Strategic recommendations include enhanced staff training programs, expansion of IoT applications, and leveraging Big Data for predictive analytics to strengthen customer relationships in the luxury hospitality sector. Limitations, such as the focus on luxury hotels and the exclusion of guest perspectives, highlight areas for future research25 0Item Restricted Impact of Artificial Intelligence Integration in Emergency Department Triage on Waiting Times: A Systematic Review Compared to Conventional Practices in ED Triage.(The University of Sheffield, 2024-09) Alhazmi, Mohammed; Miles, JemieBackground: The global issue of increased patient waiting times in healthcare facilities is a pressing concern, as it can lead to significant patient harm due to delayed access to healthcare. This research proposes the integration of artificial intelligence into emergency department triage systems as a solution to mitigate this issue. Aims: To evaluate the impact of integrating Artificial Intelligence (AI) support tools on waiting times in Emergency Departments through a systematic review of existing literature. Design: A thorough systematic review of the literature was conducted by searching electronic databases and internet search engines, including ScienceDirect, Springer, and PubMed, as well as reference lists. Studies published from January 1, 2019, to May 25, 2024, were included. Articles that did not pertain to AI, interventions that were irrelevant to emergency departments (EDs) or did not provide outcomes related to reducing waiting times either directly or indirectly, or evaluation data were excluded to ensure the quality and relevance of the included studies. Results: The analysis included ten peer-reviewed journals published after January 2019 on integrated Artificial Intelligence (AI) with emergency department triage. Recent findings suggest that integrating artificial Intelligence (AI) models into the emergency department (ED) triage processes can hold significant potential for reducing overcrowding and minimising wait times. Some studies have found that AI reduces waiting times by between 20 seconds and 30 minutes. However, a study found AI to increase waiting times for categories 3 to 5 by 2.75 to 5.33 minutes. Conclusions: This review has highlighted AI's potential to bring innovative solutions to emergency department settings. Implementing these AI-driven solutions has shown promise in enhancing healthcare delivery in the emergency department. However, further research is crucial to refine these models and ensure their practical application, underscoring the importance of continued involvement in the field.105 0