Saudi Cultural Missions Theses & Dissertations
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Item Restricted ARTIFICIAL INTELLIGENCE AND FRAUD PREVENTION IN FINANCIAL SERVICES: LEGAL AND REGULATORY PERSPECTIVES ON AI’S ROLE IN DETECTING FINANCIAL CRIME(Saudi Digital Library., 2025) Alheih, Shahad; Keller, AnatArtificial intelligence (AI) is transforming financial services by enhancing fraud detection, improving regulatory compliance, and enabling real-time monitoring of financial activities. This dissertation examines the legal and regulatory frameworks governing AI-driven fraud prevention tools, with a focus on the United Kingdom and the European Union. It explores the potential of AI to strengthen financial integrity while identifying key challenges related to transparency, accountability, and data protection. The research argues that while AI offers significant benefits in detecting and preventing financial crime, robust governance mechanisms are required to mitigate associated risks. Through doctrinal legal analysis, the study evaluates existing laws, regulatory guidance, and emerging AI-specific legislation to determine whether current frameworks adequately address the complexities of AI-based fraud prevention. The dissertation concludes that although significant progress has been made, regulatory gaps persist, particularly in relation to algorithmic bias, explainability, and cross-border data governance. Strengthening these areas is essential to ensure that AI technologies support—not undermine—the principles of fairness, legality, and financial stability.6 0Item Restricted A Study on the Perceptions of Criminology and Criminal Justice Students in Scottish Universities Regarding Using Artificial Intelligence (Al) in the Criminal Justice System (CJS).(Saudi Digital Library, 2025) Jabalawi, Bayan; Brooks-Hay, OonaArtificial intelligence (AI) increasingly influences criminal justice systems (CJSs), offering efficiency, speed, and innovative decision-making opportunities. However, it also raises critical ethical, social, and technical challenges, particularly regarding bias, transparency, accountability, and the preservation of human judgment. Despite growing literature on AI and justice, limited research has examined the perspectives of future practitioners. This study addresses that gap by exploring the understandings, perceptions, and readiness of postgraduate criminology and criminal justice students in Scottish universities toward AI integration in CJSs. The research draws on semi-structured student interviews by adopting a qualitative grounded theory approach. Data were analysed using systematic coding procedures, including open, axial, and selective coding, to identify categories and construct a theoretical storyline. The findings reveal a dual perception of AI: it is seen simultaneously as a tool for institutional efficiency and a threat to human justice. Key concerns centred on algorithmic bias, privacy violations, and the erosion of empathy, while perceived benefits included enhanced efficiency, reduced institutional burdens, and novel analytical insights. Students’ knowledge was primarily shaped by fragmented sources, such as media and limited academic exposure, amplifying fascination and mistrust. Overall, students expressed conditional readiness to engage with AI, accepting it only as a supportive assistant while emphasising the need for training, safeguards, and human oversight. The study concludes that successful integration of AI in criminal justice requires technological advancement, transparent, ethical practices, and educational reforms that reinforce trust and safeguard the human dimension of justice.11 0Item Restricted AI-Based Approaches for Respiratory Disease Detection Using Audio Signals and Imaging Data(Saudi Digital Library, 2025) Shati, Asmaa; Hassan, Ghulam Mubashar; Datta, AmitavaRespiratory diseases (RDs) remain major global health concerns, typically diagnosed through imaging and auscultation, with cough sounds also offering diagnostic cues. These methods, however, are often subjective and depend on expert interpretation. Advances in machine learning (ML) enable automated RD diagnosis, yet challenges such as limited data, high computational costs, and accessibility gaps persist, underscoring the need for innovative approaches. This thesis proposes a series of novel approaches for automated RD detection, utilizing either cough audio or CXR as input modalities, selected for their availability and affordability. These approaches integrate advanced techniques for segmentation, feature extraction, and subsequent classification, offering practical and cost-effective diagnostic solutions. Extensive evaluation on multiple open-source datasets demonstrates the effectiveness of the proposed approaches across diverse diagnostic contexts.19 0Item Restricted The impact of artificial intelligent (AI) on inventory management and cost efficiency in the supply chain.(Saudi Digital Library, 2025) almasaeed, murtadha; fang, liuThis dissertation discusses the impact of artificial intelligence on inventory management and cost efficiency. The research shows how AI improves in the supply chain such as forecasting, replenishment, and warehouse operations. At the same time, it shows the challenges facing the small and medium sized enterprises (SMEs). Furthermore, the research investigates the importance of trust and satisfaction that can affect the success of AI adoption. The study uses a quantitative survey to collect data from professionals in the supply chain from different organisations and industries. Various analyses were employed including descriptive statistics, reliability testing, t-tests, correlation, regression, and ANOVA tests. According to the findings, AI adoption has improved the inventory turnover, reduced delays, and lowered labour and logistics costs. However, there are some unexpected findings such as demand forecasting and automated replenishment, which did not show statistically significant evidence. This shows that system integration and the maturity of adoption are important to achieve all the benefits. The findings also show that trust and satisfaction have an important role. The trust showed to reduction in stockouts and the satisfaction improved as the company size and usage time increased. These findings match the Technology Acceptance Model (TAM) which shows user perception affects the adoption and the outcome of AI. In conclusion, the study shows improvement in the supply chain operations after AI adoption. However, the success of AI depends on some factors such as the company's resources, the level of adoption and how employees trust the technology. These results give the manager the full view of how to use AI effectively in the operations.9 0Item Restricted How Large Language Models are Reshaping Skills and Job Requirements for Public Health Professionals in Saudi Arabia(Saudi Digital Library, 2025) Alkhinjar, Mulfi; Palmer, PaulaContext: Large Language Models (LLMs) such as ChatGPT, Gemini, and DeepSeek are transforming professional work across sectors by enhancing information processing and decision support. In public health, these technologies offer the potential to improve efficiency, analytical capacity, and data-driven decision-making. Yet, their integration raises concerns about workforce preparedness, evolving skill requirements, and ethical oversight. In Saudi Arabia, where Vision 2030 prioritizes digital transformation in healthcare, understanding how public health professionals adapt to these technologies is vital for workforce and policy planning. Method: This exploratory mixed-methods study examined the professional impact of LLMs and the preparedness of public health professionals for their integration. The validated Shinners Artificial Intelligence Perception (SHAIP) survey, adapted for LLMs and public health, was distributed to employees of the Saudi Public Health Authority, yielding 32 complete responses. Ten semi-structured interviews further explored four constructs: professional impact, preparedness, new essential skills, and obsolete skills. Quantitative data were analyzed descriptively, and qualitative data were coded using thematic analysis. Findings: Survey results indicated that LLMs positively influence efficiency and workflow but revealed gaps in training and ethical guidance. Interview themes reinforced these findings, identifying new essential skills such as prompt engineering, digital literacy, and critical oversight, while traditional tasks like manual data entry and report drafting were viewed as increasingly automated. Conclusion: LLMs are transforming the roles of public health professionals. Successful adoption requires structured training, institutional readiness, and ethical governance. The study offers actionable recommendations to align workforce development and recruitment strategies with Saudi Vision 2030, emphasizing capacity building and responsible AI integration in public health practice.13 0Item Restricted Metadata-Centric Cybersecurity Classification: A Fair Benchmark of LLMs and Classical Models(Saudi Digital Library, 2025) Binothman, Elyas; Chaudhry, Umair BilalCybersecurity breach classification supports triage and risk response but is hindered by heterogeneous reporting, class imbalance, and limited semantic coverage in traditional pipelines. Prior work has relied on rule-based heuristics and classical models (SVM, Random Forest) with heavy feature engineering, while recent LLM studies rarely evaluate breach metadata under identical, fair splits; severity labels are often absent or not reproducibly constructed. We present a metadata-centric benchmark on the Privacy Rights Clearinghouse chronology spanning two tasks: breach-type classification and severity tiering in three and five labels, with severity derived reproducibly from native fields using a Breach Level Index style mapping. All models share one preprocessing recipe and a single stratified 80/20 train–test split. We compare parameter-efficient transformers (DistilBERT and T5 with LoRA) against tuned tabular baselines (Linear SVM, Random Forest, compact ANN). On breach type, DistilBERT achieves the strongest results (Accuracy 0.943; Macro– F1 0.840), surpassing tabular baselines. For severity, a classweighted ANN on TF–IDF and categorical features attains the highest Macro–F1 at both granularities, while T5 shows high accuracy but low Macro–F1, indicating majority-class bias. The study contributes a unified PRC schema with transparent severity construction, a fair head-to-head comparison under identical conditions, and an efficiency-oriented training recipe suitable for modest hardware.11 0Item Restricted Exploring the Impact of Talent Management Strategies on AI Adoption in Saudi Arabia’s Emerging Tech Startups: The Mediating Role of Knowledge Sharing(Saudi Digital Library, 2025) Abuhaimed, Mohammad Saad; Abdoulrahman Aljounaidi Mhd RamezSaudi Arabia's Vision 2030 emphasizes AI-driven digital transformation, yet tech startups struggle to scale AI beyond pilots. Purpose: This study examines how talent management (TM) strategies—attracting-selecting (AST), developing (DT), empowering (ET), retaining (RT), and career succession (CS)—shape AI adoption, and whether knowledge sharing (KS) mediates this relationship. Method: Using probability-based systematic random sampling of employees (n=337, N=2,308) across Saudi AI-adopting startups, the model was analyzed with PLS-SEM (SmartPLS 4). Findings: AST, DT, and ET positively affect AI adoption; RT shows no effect; CS exhibits a negative effect. KS partially mediates AST, DT, ET, and CS effects, indicating TM practices influence adoption primarily through knowledge institutionalization. Implications—Industrial: Startup leaders should integrate KS infrastructures with TM initiatives. Recommended practices: (1) cross-functional AI taskforces with rotating membership; (2) peer-learning sessions where early adopters mentor colleagues; (3) searchable repositories (wikis, Confluence) documenting implementation lessons and troubleshooting guides; (4) succession systems prioritizing collaborative knowledge transfer (mentoring, communities of practice) to prevent silos. Empirical evidence shows succession planning without KS scaffolding correlates negatively with adoption (β = -0.182, p < .01), highlighting knowledge-hoarding risks. Academic: The study extends technology-acceptance theory by integrating human-capital antecedents and positioning KS as the pivotal mediating mechanism in resource-constrained startups. Testing 16 structural paths across five TM dimensions addresses three gaps: (1) mechanistic under-specification, (2) construct aggregation bias, and (3) non-Western context neglect. The mediation framework—validated through bootstrapped indirect effects—provides a replicable blueprint for future research examining causality, moderators (industry velocity, founder literacy), and boundary conditions.36 0Item Restricted SMART TOURISM IN SAUDI ARABIA: EXPLORING THE INTEGRATION OF AI IN CULTURAL HERITAGE DESTINATIONS(Saudi Digital Library, 2025) Alotaibi, Hussain; Buhalis, DimitriosIn line with Saudi Arabia’s Vision 2030, the tourism sector is undergoing rapid transformation, with smart tourism emerging as a key pillar of innovation and development. This study investigates the integration of Artificial Intelligence (AI) technologies in cultural heritage tourism, with a focus on three significant heritage destinations: Al-Ula, Diriyah, and Historic Jeddah. While innovative tourism technologies such as AI-powered recommendation systems, augmented reality (AR), and sentiment analysis have the potential to enhance tourist experiences, increase visitor satisfaction, and support heritage preservation, their adoption within Saudi Arabia’s heritage sector remains underexplored. This research aims to assess international tourists’ perceptions of AI usefulness, satisfaction, and trust, and to examine their behavioural intentions and willingness to pay for AI-enhanced services. A quantitative survey method was employed, with a sample of 306 international tourists who interacted with AI services at the selected heritage sites. Data were analysed using frequency distribution, descriptive statistics, reliability analysis, ANOVA, and correlation tests. The findings are expected to provide empirical insights into the effectiveness of AI technologies in enhancing cultural tourism experiences while preserving authenticity. The study offers practical implications for tourism authorities, technology developers, and policymakers on how to strengthen innovative heritage tourism strategies in Saudi Arabia.25 0Item Restricted The Role of Artificial Intelligence in Monitoring the Quality of Construction Project (Case Study) in Al-Jouf Region(Saudi Digital Library, 2025) Alsharari, Abdulmohsen; AlBtoush, MuhammedThis study investigates the current status and potential of Artificial Intelligence (AI) applications in Construction Engineering and Management (CEM) in the Al-Jouf region of Saudi Arabia. AI has demonstrated significant promise in enhancing resource use, project performance, and quality control in a number of industries; however, Al-Jouf's implementation of AI is beset by particular difficulties, including a lack of funding, a lack of skilled AI specialists, and limited infrastructure. The study uses a descriptive-analytical methodology to fill this gap, combining a review of the literature with primary data gathered from stakeholders in ongoing construction projects in Al-Jouf using a structured questionnaire. The approach includes both secondary sources (academic literature, journals, and reports) and primary sources (field responses from engineers, project managers, and administrative personnel). Data analysis was conducted using SPSS software to evaluate AI awareness, current applications, challenges, and impacts on construction quality dimensions—namely technical compliance, time performance, cost control, and customer satisfaction. The structured questionnaire was designed based on a pilot test and adapted to reflect local construction sector dynamics. Non-probability purposive sampling was used to ensure the selection of knowledgeable participants. The study offers practical insights and recommendations to policymakers, engineers, and industry stakeholders for facilitating AI adoption, aligning it with local project requirements, and supporting sustainable development in the construction sector. The study results indicate a generally positive impression of AI in the construction sector in Al-Jouf region, with a high average score of 3.76. The field “Impact of AI on the Quality of Construction Projects” received the highest score (M = 3.97), while the second field, “Challenges of Applying AI in Construction Projects” received a high score (M = 3.93). The field “Using AI in Project Quality Control” also received a high score (M = 3.76). The lowest-rated field was “Awareness of AI in the Construction Sector” (M = 3.37). The study recommended promoting a culture of artificial intelligence in the sector through collaboration between government agencies and educational institutions with construction companies to ensure regular workshops, seminars, and technical training courses on the fundamentals of artificial intelligence and its applications in the construction sector.21 0Item Restricted A CLOUD-BASED AI SYSTEM FOR SKILL GAP ANALYSIS AND TRAINING PATH RECOMMENDATION IN HR DEPARTMENTS(Saudi Digital Library, 2025) Alanazi, Abdullah Ramadan; AlYamani, AbdulghaniThis dissertation presents the development of a cloud-based artificial intelligence (AI) system designed to automate skill gap analysis and provide personalised training recommendations in Human Resource (HR) departments. The system integrates employee profiles, job role requirements, and training histories to identify competency gaps using a decision tree algorithm. The AI model achieved an accuracy of 0.86 and demonstrated strong interpretability and efficiency in recommending relevant training paths. Usability testing with HR professionals confirmed the system’s practicality and reliability in supporting workforce development and data-driven training strategies. The research contributes to the field of HR analytics by combining Human Capital Theory with Knowledge Discovery in Databases (KDD) to provide an explainable, scalable, and cloud-enabled HR decision-support framework.10 0