Saudi Cultural Missions Theses & Dissertations
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Item Restricted AI-Related Harm in Saudi Private Law(Saudi Digital Library, 2026) Aldhowayan, Mansour; Li, ShuThis thesis examines whether Saudi private law can adequately address harm caused by autonomous and opaque artificial intelligence systems. It argues that AI-related harm may create a structural compensation gap where victims cannot easily identify a responsible actor, prove fault, or establish causation under ordinary liability rules. Using EU AI governance and product-liability frameworks as a functional benchmark, the thesis shows the limits of individualised liability in responding to such harm. It then argues that Sharia principles, particularly harm removal and benefit-based burden allocation, can justify supplementary collective redress under Saudi private law. The thesis proposes a dual-track model combining a limited zakat- based safety net with a broader state-backed or industry-funded compensation mechanism.3 0Item Restricted Exploring and Designing AI-Based Technologies for Mental Health(Saudi Digital Library, 2026) Alotaibi, Abeer; Corina, SasOver the past decade, Human–Computer Interaction (HCI) research has seen a growing interest in mental health technologies. At the same time, advances in Artificial Intelligence (AI) have introduced new opportunities and challenges for the design of ethical, transparent, and clinically relevant tools to support mental health practitioners in assessment, diagnosis, and intervention. This thesis explores the intersection of AI and HCI in mental health, focusing on how AI-based technologies can be designed, understood, and evaluated to ethically and effectively support practitioners in identifying mental health conditions. The research adopts a multi-stage design-oriented methodology structured around three key phases: understanding the AI–HCI design space, exploring the ethical design of novel AI–HCI technologies, and evaluating the designed AI–HCI solutions. The first phase investigates how AI-based technologies are currently used within mental health. Chapter 4 presents findings from a functionality review of 13 AI-based mental health mobile applications. The analysis employed expert evaluation to explore each app’s AI support, functionality, purpose, and ethical implications. Findings showed that most apps use AI to track moods and emotions, generate personalized well-being recommendations, and provide conversational support through Natural Language Processing (NLP)-based agents. However, the review also showed significant limitations in AI literacy support, explainability, and transparency, as well as a general lack of ethical design considerations regarding data reliability, consent, and algorithmic bias. These insights highlight the need for human-centered and ethically grounded AI design frameworks for mental health. Chapter 5 presents key findings from interviews with mental health practitioners (n = 18). It further examines how they conceptualize the role of AI across the key stages of therapeutic practice: assessment, diagnosis, and treatment. Ethical concerns related to privacy, explainability, and inclusivity were systematically analysed based on core biomedical ethics principles: non-maleficence, beneficence, justice, and autonomy, translating practitioner insights into actionable, ethically grounded design solutions to guide the development and integration of AI in mental health practice. The chapter contributes: (i) stage-specific conceptualization of AI’s role; (ii) systematic organization of ethical concerns; and (iii) novel design implications. These findings shift focus from whether to how AI can be responsibly designed as longitudinal clinical support throughout therapy. Building on the insights from Chapter 5, the second phase, Chapter 6, focuses on designing and developing ethical AI–HCI technologies to support depression symptom identification using Large Language Models (LLMs). The first iteration, DepressionSymp (Model 1), showed strong accuracy (ROUGE-1 = 0.9154) across five categories: risk, symptoms, time, lifestyle, and other medical conditions. The model was implemented to identify symptoms and help clinical practitioners with diagnosis and decision-making. Chapter 7 presents Workshop 1, in which mental health practitioners (n = 6) engaged in evaluating and co-designing DepressionSymp (Model 1). Practitioners evaluate the system across four AI ethical principles: accuracy, reliability, interpretability, and trust, as well as broader ethical design considerations. They also provided recommendations for improvements to ensure the model is used safely and responsibly in clinical practice. Responding to the insights from Chapter 7, Chapter 8 details the iterative redesign of the DepressionSymp (Model 2). By using a prompt-based learning approach, the model showed improved contextual understanding, interpretability, and trust (ROUGE-1 = 0.8486). Finally, Chapter 9 presents Workshop 2, in which mental health practitioners (n = 8) engaged in evaluating and co-designing DepressionSymp (Model 2). The workshop used the same AI ethical principles as in Workshop 1. Practitioners highlighted improvements in trust, transparency, and clinical applicability, and identified further opportunities to enhance interpretability and trust in the risk identification category. Both workshops demonstrate how iterative, practitioner centered design can create LLM-based systems that ethically support mental health practitioners. Overall, this thesis contributes to AI-HCI and mental health research by demonstrating how ethical, practitioner-informed AI systems can be designed to support clinical decision-making.25 0Item Restricted Modeling Learners’ Cognition and Metacognition In Data Science Problem Solving(Saudi Digital Library, 2026) Alomair, Maryam M; Shimei, Pan; Lujie, Karen ChenIn today's age of big data, data scientists —a group of specialists trained to process and analyze large, complex amounts of data and interpret and communicate results to inform decision-making processes in interdisciplinary fields —are in high demand. A competent data scientist should be fluent in declarative knowledge (knowing facts) about data science methods, procedural knowledge (knowing how to do things) of executing the methods, and conditional knowledge (knowing when and why to apply specific methods) to address problems effectively. In addition to these cognitive competencies, a data scientist needs to be equipped with metacognitive capabilities (thinking about thinking) to effectively plan, monitor, and regulate the problem-solving processes. As AI tools like ChatGPT become increasingly capable of performing routine, well-specified tasks, training data science learners to be equipped with higher-order Data Science Problem Solving (DSPS) skills and the relevant metacognitive competency becomes critically urgent. Despite the urgent need for DSPS training, current data science education programs often focus on declarative and procedural knowledge in the cognitive domain but lack explicit coaching in high-order problem-solving and related metacognitive skills. Furthermore, the traditional mentorship approach with experienced data scientists that guide practice and make expert thinking visible, inspired by “cognitive apprenticeship,” struggles to scale due to a limited supply of qualified mentors. Consequently, this demand and supply gap may create potential equity concerns, as the scarce mentoring resources will likely be unevenly distributed. In light of these challenges, alternative approaches are needed. This dissertation is built upon an existing case-based learning tool, Caselet that focusing on the training of DSPS that require critical thinking and metacognitive skills besides declarative and procedural knowledge. Additionally, by tracking and modeling learners' cognitive and metacognitive progress, this work lays the foundation for a future student model that could be a key component of an intelligent tutoring system (ITS). An ITS is a computer-based system designed to provide personalized and adaptive instruction to learners, mimicking the guidance and support typically offered by human tutors. This kind of system could potentially address the second challenge, namely, the shortage of qualified mentors. Leveraging machine learning and learning analytics to model and track learners' cognition and metacognition in DSPS tasks will be achieved through three primary objectives: (1) building models of cognitive and metacognitive processes while learners are engaged in DSPS activities and (2) exploring the role of cognition and metacognition in predicting learning outcomes. By understanding the cognitive and metacognitive processes employed by learners during DSPS tasks and elucidating the connection between learners' cognition and metacognition, this dissertation aims to inform the design of student model within ITS to facilitate large-scale individualized DSPS learning and, ultimately, enhance the education of competent data science problem solvers in the era of AI.8 0Item Restricted Impact of AI Adoption on Engineering Management(Saudi Digital Library, 2026) Almutairi, Naif Meshal; Milan, SimicArtificial Intelligence (AI) is reshaping engineering management, yet the literature offers a fragmented account of how AI adoption interacts with leadership, organisational culture, workforce adaptation, and governance in engineering settings. This dissertation addresses that gap by examining AI adoption across five interrelated dimensions: managerial decision-making, project-delivery efficiency, leadership and organisational readiness, organisational culture and workforce adaptation, and implementation risk. Adopting an interpretivist philosophy and inductive qualitative design, the study synthesises forty-seven peer-reviewed sources (2019–2026) drawn from Scopus, Web of Science, Google Scholar, IEEE, and Emerald, analysed thematically to identify recurring patterns of organisational behaviour and managerial experience. The findings show that AI strengthens managerial decision-making — improving forecasting, resource allocation, scheduling, and predictive maintenance — but only where managerial judgement remains the locus of accountability; that AI improves project-delivery efficiency through automation and real-time monitoring, constrained by legacy-system integration and uneven digital maturity; and that AI adoption is fundamentally a socio-technical transformation in which leadership behaviour, organisational readiness, workforce capability, and ethical governance jointly determine whether technological capability converts into sustained organisational benefit. Building on these findings, the study develops a refined conceptual framework — AI adoption outcomes = f(Technological capability × Leadership behaviour × Organisational conditions × Workforce adaptation) — moderated by two constructs the dissertation contributes to the literature: governance assurance and managerial augmentation logic. These constructs extend established adoption theories (TAM, TOE, DOI, STST) and explain why organisations with comparable technological capability achieve markedly different adoption outcomes. The dissertation concludes with practical recommendations for engineering leaders, project managers, and policymakers seeking to adopt AI responsibly, and identifies directions for future empirical research to test the framework.26 0Item Restricted The Architecture of Opportunity: An AI-Powered Approach for Large-Scale Discovery and Recommendation(Saudi Digital Library, 2026) Alotaibi, Naif Nasser N; Saberi, Morteza; Hussain, Farookh Khadeer; Bandara, MadhushiResearch organisations often struggle to plan proactively because signals about opportunities are dispersed across heterogeneous and largely unstructured web sources (e.g., researcher profiles, grant portals, and public announcements). Traditional tools such as SWOT support reflection, but they rarely operationalise these signals into measurable factors that enable continuous, data-driven opportunity discovery and recommendation. This thesis develops an end-to-end framework that transforms web-derived unstructured information into high-quality stakeholder opportunity datasets and recommendation outputs. The thesis is organised into three stages. Stage 1 (Data Collection and Data Quality) defines a unified stakeholder and opportunity data model and develops an extraction pipeline that integrates prompt engineering and few-shot GenAI extraction with traditional ML and rule-based validation to populate feature values from unstructured sources. It further improves dataset reliability through data cleaning, normalisation, and ML-based imputation of missing stakeholder and opportunity features. Stage 2 (Recommendation for a Known Opportunity) investigates the setting where the opportunity is given (e.g., a specific grant). It designs and evaluates models that rank and recommend the most relevant stakeholders using evidence derived from both structured fields and unstructured text, leveraging pre-trained language models to represent and retrieve matching signals. Stage 3 (Recommendation for an Unknown Opportunity) addresses the discovery of opportunities that are not provided in advance. It develops and validates methods to identify previously unseen opportunities from external signals and recommend them to suitable stakeholders. Overall, the thesis contributes a practical pipeline from web data to opportunity matching and opportunity discovery for research and educational institutions.22 0Item 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.22 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.28 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.29 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 0