Saudi Cultural Missions Theses & Dissertations
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Item Restricted CYBERSECURITY STANDARDS FOR AGENTIC AI SYSTEMS(Saudi Digital Library, 2025) AlAlmai, Ahmed; Zaki, HamdaniThis project examines the cybersecurity challenges associated with Agentic Artificial Intelligence (AI) systems, which are capable of autonomous decision-making and adaptive behaviour. It evaluates the limitations of existing cybersecurity and governance frameworks, with particular emphasis on ISO/IEC 42001, in addressing emerging AI-specific threats. The study investigates key risks including adversarial machine learning, data poisoning, model inversion, unauthorized model use, and ethical concerns such as bias and transparency. Based on these findings, the project proposes practical security enhancements, including adversarial testing, secure data pipelines, robust access controls, explainable AI techniques, continuous monitoring, and AI-specific governance policies. The report also highlights the importance of lifecycle-based security management and awareness programs to improve organizational resilience against evolving AI threats. The findings provide a foundation for developing secure, trustworthy, and responsible Agentic AI systems while supporting future technical implementation and compliance with emerging AI security standards.7 0Item Restricted Engineering and Classifying Indirect Prompt Injections: A Socio-Technical Approach to AI Agent Security(Saudi Digital Library, 2026) Aldurayhim, Abdulaziz; Abudayeh, MohammadAs autonomous AI agents become more prevalent in enterprise settings, they are now susceptible to "Indirect Prompt Injection (IPI)" attacks, which embed adversarial payloads in external data sources to manipulate the agent's contextual execution and force unauthorised actions. This dissertation holds that IPI attacks constitute a socio-technical phenomenon because they exploit semantic vulnerabilities in agent architectures and psychological manipulation patterns in human communication. To address this threat, the study proposes a three-dimensional taxonomy based on payload type, injection vector, and social engineering trigger. The initial corpus comprised 1,119 instances; following rigorous preprocessing and deduplication, a refined dataset of 858 validated samples was established for modelling. A separate 1,032-sample verified dataset pack was used only as an independent reference for dataset verification, not as the definitive modelling dataset. A DeBERTa-v3-base classifier is fine-tuned to classify social engineering triggers, using RoBERTa-base as a baseline. On the OSINT subset, DeBERTa's macro-F1 score was 0.983, and the accuracy was 91.7%. The study also presents a probabilistic Confused Deputy threat model and an ACE defence architecture consisting of architectural controls, runtime detection, privilege minimisation, human-in-the-loop oversight, and practitioner education. The findings conclusively demonstrate that integrating advanced NLP detection mechanisms with socio-technical security frameworks significantly enhances enterprise resilience against IPI threats.10 0Item Restricted The risk of artificial intelligence in academic: investigating students dependency on artificial intelligence for assignments and exams(Saudi digital library, 2025) Alnefaie, Rasil; Koutsoulis, MichalisArtificial intelligence (AI) tools like ChatGPT are experiencing rapid adoption in higher education institutions, which is changing students' approaches to academic work. The tools provide benefits through increased efficiency and improved accessibility but they create problems because students develop dependency which affects their academic performance. The research study investigates how much students depend on artificial intelligence for their academic tasks which include assignments and exams while assessing its effects on their cognitive abilities and academic honesty. The researchers used a quantitative research method to conduct a study which involved 100 University of Nicosia students who completed a structured questionnaire that measured their AI usage and cognitive effects and integrity risks and trust in AI. The research results show that many students need to use AI tools because they face difficult academic situations which create urgent deadlines. Many respondents reported negative effects on critical thinking, creativity, memory retention, and depth of learning. Respondents widely confirmed their worries about academic integrity issues which included plagiarism and decreased originality. Students demonstrate average trust in AI tools but they require both institutional guidelines and training to trust those systems fully. The research shows that academic performance receives assistance from AI tools yet students who depend too much on these tools lose crucial learning abilities therefore academic institutions need to implement responsible AI usage policies that achieve educational results.6 0Item Restricted Essays on Artificial Intelligence and Firm Performance(Saudi Digital Library, 2026) Obaidan, Ali; Banerjee, Sourindra; Heinberg, Martin; Katsikeas, CostasArtificial Intelligence (AI) is widely regarded as the latest General-Purpose Technology (GPT) to emerge. This characterisation places AI alongside transformative innovations like the steam engine, electricity, and the internet. If such characterisation is correct, AI adoption is likely to have significant implications for firm performance. For instance, AI can enhance firms’ ability to innovate and commercialise innovations while improving productivity through reduced labour intensity and operational disruptions. However, as with prior GPTs, realising AI’s full potential requires complementary strategic and organisational adjustments that are inherently lengthy, uncertain, and costly. I assess this theory through two complementary studies. In the first, I investigate whether firms’ strategic focus on revenue growth versus cost efficiency with AI leads to differential performance outcomes. To do so, I analyse over 2 million AI-related contextual windows from firms’ annual reports to infer their AI strategic focus. My results show that focusing on cost efficiency with AI improves firm performance whereas focusing on revenue growth negatively affects performance outcomes. Moreover, I find that marketing and operations capabilities positively moderate the effect of AI revenue focus and negatively moderate the effect of AI cost focus. In the second study, I investigate the effects of hiring AI-skilled employees on firms’ innovation and marketing capabilities. In doing so, I draw on a unique dataset tracking the presence of AI-skilled employees in U.S. firms through textual analysis of their resumes. The results indicate that hiring AI-skilled employees enhances firm performance through its positive impact on innovation capability while simultaneously undermining performance through its negative effect on marketing capability. Moreover, the analysis reveals that financial slack plays a critical role in positively moderating these effects. Overall, my findings suggest AI’s impact on firm performance depends on various complementary resource investments and functional capabilities, making it far more complex than is commonly assumed.8 0Item Restricted Designing a Customer Experience Framework to Optimise the Impact of AI Adoption in the Retail Sector(Saudi Digital Library, 2026) Alenazi, Mohammed; Yinshan, Tang; Gulliver, StephenThe use of artificial intelligence (AI) in retailing is widely recognised as a significant catalyst for operational, supply chain and consumer behavioural change. However, despite substantial investment in AI, several retail organisations are struggling to achieve the desired customer experience and firm performance. While extant literature explores AI adoption or customer experience, very few studies discuss the relationship between backend operational efficiency enabled by AI and frontend customer experience. The aim of this study is to address this research gap by proposing an overarching framework of customer experience that maximises the effect of AI adoption in the retail sector. This study adopts the socio-technical perspective and an extended Leavitt Diamond Model to conceptualise AI adoption as a change agent that affects four interconnected organisational elements, namely, people, task, technology, and structure. A mixed methods approach is employed for data collection and analysis. The quantitative survey data from 390 retail employees and consumers were analysed using SPSS and Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the inter-relationships between AI adoption and some organisational and customer-related constructs. In addition, semi-structured interviews with retail managers and employees were conducted to gather insights into AI adoption implementation, operational challenges and customer experience. The reflexive thematic analysis is employed to analyse the qualitative data and triangulate with the quantitative data. The results suggest that AI adoption has a significant impact on customer purchasing behaviour, inventory management efficiency, employee productivity, shopping and task efficiency and customer experience. However, implementation, data integration and security are major constraints to successful AI adoption. Moreover, this study confirms that backend operational efficiency has a positive relationship with customer experience, which suggests that organisational efficiency plays a crucial role in enhancing customer experience. This study contributes to the extant literature primarily by extending the Leavitt Diamond Model within the context of AI adoption in the retail sector. The findings provide empirical evidence linking v AI-enabled operational efficiency to customer experience and demonstrate how the interdependent dimensions of structure, people, technology, and tasks shape AI-enabled organisational transformation. In addition, the study develops an evidence-informed practical AI adoption framework to support retail organisations in implementing AI initiatives that enhance both operational performance and customer experience. The study suggests that customer experience should be configured as an integrated framework of interdependent constructs. The framework can be used by practitioners as a tool to evaluate their readiness for AI adoption and manage the challenges associated with AI adoption. Moreover, the framework will help retail decision makers to ensure that AI adoption contributes to sustainable value for both organisations and their customers. This study argues that AI adoption should be considered as an organisation-wide initiative rather than a technology only solution. Future studies may investigate the longitudinal effect of AI adoption, governance and ethical issues in AI-enabled retailing and apply the proposed framework in other service industries.17 0Item Restricted Using Artificial Intelligence (AI) to Improve the Identification of Gifted Students(Saudi Digital Library, 2025) Almalki, Hamid Ahmad; Beck, Dennis EThe present study examined teachers' perceptions of using artificial intelligence (AI) to identify gifted students. The issue is clearly relevant today; several schools continue to grapple with unfair practices and irregular assessment processes, most notably for culturally and linguistically different students. Conventional keys and expert opinion remain essential, but their limitations have been acknowledged. Newer research suggests that AI could actually improve the system by making it more efficient and able to pool data that originally surfaced biases. At the same time, issues of transparency, data privacy, and the ethics of decisions driven by AI remain unresolved. To gain an understanding of this, a total of 14 educators for K-6 in the gifted field responded to an open-ended survey about their perceptions of AI for identification technologies. The analysis revealed seven main themes: current challenges, anticipated benefits of AI, concerns and ethical dilemmas, needed supports, barriers to implementation, expected outcomes, and recommendations for leaders. Teachers perceived value in AI for efficiency and objective identification. However, they were concerned about the interpretation of results and the ethical monitoring of its use. The results show that although teachers have a positive outlook on AI, given its potential, it is also critical to provide clear guidelines and training on AI in schools so that this technology can be used responsibly. These findings provide a starting point for future research on designing ethical and fair AI-assisted methods for K-12 identification of gifted students.28 0Item Restricted AI-Enabled Autonomous Knowledge Extraction from Large-Scale Textual Data(Saudi Digital Library, 2026) Alharbi, Abdulrahman; Obradovic, ZoranThe rapid growth of large-scale textual data across social media platforms, news media, and scientific repositories presents both unprecedented opportunities and significant challenges for extracting meaningful insights. During global events such as the COVID-19 pandemic, understanding public discourse requires analyzing vast amounts of noisy, heterogeneous, dynamic, and geographically distributed data. At the same time, the exponential increase in scientific publications has made traditional evidence synthesis methods increasingly labor-intensive, time-consuming, and difficult to scale. Existing approaches to textual knowledge extraction often operate in isolation, lack interpretability, fail to integrate heterogeneous data sources, and do not support scalable end-to-end automation. This dissertation addresses these limitations by proposing a unified framework for AI-enabled autonomous knowledge extraction from large-scale textual data. The research introduces a comprehensive pipeline that integrate sentiment analysis, topic modeling, semantic interpretation, spatiotemporal reasoning, and multi-agent automation for scalable, robust and reproducible text analysis across heterogeneous domains. First, this work introduces TriLex, a novel unsupervised sentiment analysis framework that combines multiple lexicon-based sentiment analysis methods through weighted aggregation, majority voting, and dynamic thresholding technique to improve robustness and accuracy for short and noisy textual data. Building on this foundation, a hierarchical spatiotemporal framework is developed to capture the evolution of public sentiment across global, national, and regional scales. The framework integrates over 7 million social media posts and thousands of news articles to analyze COVID-19 vaccine discourse across time, geographic regions, and platforms. To enhance topic interpretability, this research integrates BERTopic with large language models (LLMs), enabling automated generation of coherent and context-aware topic representations for large-scale textual discourse. A cross-platform analytical framework is further introduced to examine temporal relationships between social media and news media discourse, demonstrating a bidirectional relationship in which news coverage and public discourse influence each other over time. Extending beyond discourse analysis, this dissertation introduces an Agentic AI framework that automates the end-to-end process of large-scale multilingual knowledge extraction and evidence synthesis. The proposed multi-agent system coordinates specialized agents for query generation, multilingual retrieval, metadata harmonization, title and abstract screening, and full-text analysis. Evaluated on a multilingual corpus of over 52,000 scientific records, the framework achieves high screening performance while substantially reducing processing time from months to hours, demonstrating significant improvements in scalability, robustness, and reproducibility. Collectively, this dissertation bridges the gap between analytical understanding and autonomous knowledge extraction from large-scale textual data. By integrating robust sentiment analysis, interpretable topic modeling, spatiotemporal discourse analysis, and autonomous AI systems within a unified framework, this work establishes a scalable and extensible paradigm for transforming heterogeneous textual data into actionable knowledge. The proposed methodologies are validated using real-world datasets spanning social media, news media, and scientific literature across diverse application domains.13 0Item Restricted Artificial intelligence for bias detection in higher education online content(Saudi Digital Library, 2026) Bin Shiha, Rawan; Eric, Atwell; Noorhan, AbbasThis thesis develops and evaluates Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches for detecting bias in higher education online resources, addressing the lack of systematic computational methods in this area. Bias in higher education content shapes knowledge production, representation, and equity, making its detection both academically and socially significant. To address this gap, three sets of novel datasets were created: 1. a corpus of university news articles annotated for subjectivity, sentiment, and gender representation; 2. three university reading list datasets with demographic annotations enabling comparative analysis across Western and Middle Eastern contexts; and 3. two domain-specific collections of learning materials, one from humanities-oriented open resources and the other from Science, Technology, Engineering, and Mathematics (STEM) lecture transcripts. Together, these datasets provide the first systematic resources for investigating representational, stereotypical and linguistic bias across diverse higher education domains. Using these resources, a range of Pre-trained Language Models (PLMs) and Large Language Models (LLMs) were evaluated for bias detection. PLM revealed significant gendered and representational disparities in university discourse, while fine-tuned LLM achieved improved performance on humanities data but showed limited transferability to STEM materials. A hybrid framework integrating fine-tuned LLMs with Retrieval-Augmented Generation (RAG) enhanced detection transparency and prediction balance. These methods were operationalised in a prototype web application for bias detection in higher education learning content. The contributions of this research are fourfold: 1. the creation of multiple novel annotated datasets spanning university news, reading lists, and academic learning resources; 2. the introduction of LLM-based strategies for bias annotation, fine-tuning, and cross-domain evaluation 3. methodological innovations including a structured framework for categorising bias, a hybrid human–AI annotation approach, and a replicable NLP pipeline for demographic and thematic analysis; and 4. the design of a hybrid bias detection system and accompanying web application. This work advances computational approaches to bias detection, provides reproducible resources for future research, and offers practical tools to support greater fairness and equity in higher education online environments.3 0Item Restricted Artificial Intelligence in Routine Non-contrast CT Imaging to Assess Cardiothoracic Structures and Evaluate Clinical Utility(Saudi Digital Library, 2026) Alnasser, Turki; Swift, Andrew; Alabed, SamerBackground: Pulmonary hypertension (PH) is a progressive and life-threatening condition characterised by elevated pulmonary arterial pressure and associated with different diseases, including left heart and lung diseases. Early diagnosis is essential to improve clinical outcomes; however, current diagnostic pathways rely on invasive right heart catheterisation or contrast-enhanced imaging, which are not always feasible in routine clinical practice and are associated with different complications. Non-contrast chest computed tomography (CT) is widely available and frequently performed in patients with PH, many of whom also exhibit coronary artery calcification. However, its full diagnostic and prognostic potentials remain underexplored. Although several manually derived CT measurements have been proposed as imaging predictors, their clinical utility is limited by inter-observer variability and time-consuming analysis. Aim: This thesis investigates the use of artificial intelligence (AI)–driven volumetric measurements of multi-cardiothoracic structures from non-gated, non-contrast CT to enhance both the diagnosis and prognostic assessment of PH and coronary artery calcification. Methods: A multi-cardiothoracic structure and coronary artery calcification AI-based segmentation models were developed at the University of Sheffield using internal and external cohorts from the ASPIRE registry. The models were benchmarked against the gold standard haemodynamic, reference standards, visual assessments, and validated tool (e.g. TotalSegmentator). Results : The developed AI-based models demonstrate high diagnostic accuracy in predicting PH and detecting coronary artery calcifications. The models were comparable to TotalSegmentator and demonstrated higher accuracy than manual clinical practice techniques when evaluated in exploratory testing. AI-derived right atrial volume and coronary artery calcifications are independently associated with increased mortality in PH patients, even after adjustment for age, sex, PH subgroups, and REVEAL score. Conclusion: Automated segmentation and volumetric measurements of multi-cardiothoracic structures in non-contrast CT have the potential to facilitate earlier and accurate diagnosis and prognostic assessment of coronary artery calcification and PH patients, including lung and left heart diseases.18 0Item Restricted Exploring the Utilization of Multimodal LLMs on Personalized Learning in Higher Education(Saudi Digital Library, 2025) Aljumaah, Jana Abdullah; Rana, Muhammad; Rizwan, TaimoorUse of Large Language Models (LLM) is rising in popularity amongst students in higher education. It has transformed the way students tackle their academic work, offering stu- dents powerful new tools for explanation, idea generation, writing support, and independent study. While their adoption has been widely discussed in theory, there remains limited empirical evidence on how students are using these technologies, how they per- ceive their benefits and risks, and how such practices may reshape learning. This dissertation explores the perceptions and practices of 55 UK university students re- garding the use of LLMs in academic work. Using a survey-based methodology, the study examines five key objectives: to identify patterns of LLM use, analyze the purposes for which they are employed, evaluate student attitudes toward their advantages and limita- tions, assess their influence on study habits and independent learning, and provide insights for ethical and effective integration into higher education. The results reveals that students use LLMs as tools for understanding and organizing knowledge such as summarization or concept explanations and clarifications. Secondly, they employ them for supporting their academic writing and production like proofreading, editing, or as a writing assistant. Likert-scale analyses indicate that students generally per- ceive LLMs as both useful and easy to use, consistent with the Technology Acceptance Model (TAM). However, concerns around trust and over-reliance temper this enthusiasm, highlighting a critical awareness of the risks involved. The findings suggest that while LLMs are integrated into study habits and promote efficiency, their long-term value will depend on guidance, ethical frameworks, and digital literacy training. The study contributes to the growing body of research on AI in education by offering evi- dence from the UK universities context. It demonstrates how perceptions of usefulness, ease of use, and reliability shape adoption, and it paves the way for future research, includ- ing cross-cultural comparisons, to better understand the evolving role of LLMs in higher education.40 0