Saudi Cultural Missions Theses & Dissertations
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Item Restricted ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE TEACHING AND LEARNING: LEVERAGING AI FOR LEARNER SUPPORT AND TEACHER DEVELOPMENT(Saudi Digital Library, 2025) Alyobi, Mazen; Egbert, JoyThis dissertation explores the emerging role of artificial intelligence (AI) technologies in English language teaching and learning. The dissertation comprises two complementary studies. The first study is a systematic review, utilizing the PRISMA model, that examines empirical research studies on the use of intelligent personal assistant (IPA) tools in an English as a foreign language (EFL) context. It focuses on the types of IPAs implemented, the language skills the studies target, IPA effectiveness for language learners, and the challenges students encountered. The findings revealed that the most commonly utilized IPAs in the EFL context were Google Assistant and Alexa. It also highlights that IPA use helped EFL learners improve their oral, listening, and pronunciation skills in several studies. The analysis found that speaking and listening skills were the most frequently targeted in the included studies, with positive effects, as well as students’ overall positive perceptions. However, the systematic review shed light on some limitations of IPA use, including errors in detecting pronunciation, students’ accents, and other technological issues. The second study is an exploratory case study that examines three English language educators’ usage and experiences with an automated feedback tool to support reflective teaching (RT). It investigates whether those experiences led to changes in their teaching practices and what changes were made. Data were collected from background surveys, self-reflection questions, semi-structured interviews, and automated feedback tool reports. The findings indicated that participants had a positive perception of using automated feedback to support RT, and they primarily used the automated feedback to increase their awareness of classroom interactions. The data revealed a measurable change in reducing teacher talk time and increasing student talk time for two of the teachers, while other instructional strategies showed mixed results. However, EL teachers expressed concerns regarding the accuracy of automated feedback in detecting nuanced interactions. In sum, while AI integration in these two studies showed some positive outcomes, the reported AI limitations may hinder its use due to limitations such as AI detection accuracy for diverse language classrooms. However, the two studies holistically provide insights into AI integration in English language teaching and learning, and they contribute to the growing body of knowledge on AI in language education.25 0Item Restricted Explainable Goal Recognition Systems(Saudi Digital Library, 2025) Alshehri, Abeer; Vered, MorThis thesis explores human-centered approaches to explaining and understanding why goal recognition (GR) agents predict specific goal hypotheses. Goal recognition is the process of inferring an agent’s hidden goal from its observed behaviour, playing a crucial role in AI with various practical applications. Since the field’s inception, understanding the behaviour, decisions, and actions of ar- tificial intelligence (AI) agents has been a core focus of research. As these systems grow increasingly complex, their reasoning processes often become opaque to end users, raising significant challenges in high-stakes and collaborative environments. Lack of transparency can undermine trust and hinder effective decision-making. Enhancing au- tonomous agents’ explainability is vital, enabling users to comprehend and trust the reasoning behind these systems’ predictions. Understanding how humans generate, select, and convey explanations can serve as a ba- sis for developing effective explainable agents. Explaining the behaviour and predictions of GR agents engaged in sequential decision-making presents unique challenges. Tra- ditional approaches to explainability often focus on aligning an agent’s behaviour with an observer’s expectations or making the reasoning behind decisions more transparent. Building on insights from cognitive science and philosophy, this thesis delves deeper into understanding the nature of explanations within human cognition. The central contribution of this work is the introduction of the eXplainable Goal Recog- nition (XGR) model, a novel framework that generates counterfactual explanations for GR agents. The XGR model addresses “why” and “why not” questions by leverag- ing insights from two human-agent studies and proposing a conceptual framework for human-centred explanations of GR. Building on these foundations, the thesis extends the XGR model by introducing the Hypothesis-Driven XGR model, which integrates the emerging decision-making paradigm of Evaluative AI. Our empirical evaluations demon- strate that the proposed models enhance trust in GR agents and effectively support user decision-making, outperforming baseline approaches across key domains. This research presents the first systematic investigation into human-centred explanations for goal recognition systems in sequential decision-making domains. It advances the field of explainable AI and provides practical methods to improve user understanding and trust in GR systems.6 0Item Restricted Enhancing Colorectal Cancer Automatic Diagnosis using Artificial Intelligence Techniques Depending on Preferable Medical Scan(Saudi Digital Library, 2025) ALSULAIMAN, IBRAHIM ALI M; Elgarayh, AhmediOne of the most common causes of death is colorectal cancer (CRC). The spread of cancer cells to other organs increases dramatically because of delayed detection. Presently, the only ways to increase survival rates and reduce cancerrelated mortality are via prompt diagnosis and customized therapies. Artificial intelligence (AI) may significantly aid professionals in identifying CRC cases with less effort, time, and cost. This study presents a novel convolutional neural network (CNN) for detection known as COCDNet and two sets of modifications to CNN models for identifying cecum CRC in computed tomography (CT) radiological scans. Before images are included in the architecture, they are preprocessed to reduce the noise. The data is then sent into a COCDNet model that holds 22 layers. On other hand, two types of transfer learning (TL) are used in four popular CNN models: DarkNet19, VGG16, VGG19, and AlexNet. The dataset comprises 1,695 images of abdomen CT scans, categorized into two main classes as cecum cancer and normal images. COCDNet achieves the highest performance, proving an accuracy of 97.04%, an F1-score of 95.80%, and recall approaching 100%. These measures demonstrate that COCDNet is a dependable tool for early CRC diagnosis because it can both reliably detect cancer and reduce false positives. The suggested model success in detecting cecum CRC demonstrates the value of this work that improves AI models for bettering healthcare systems and saving lives.21 0Item Restricted AI Impersonation on social media Analysing Human Characteristics and Ethical Implications(Saudi Digital Library, 2025) Almuammar, Eyad; Fahad, AhmadThis study explores the behavioural, ethical, social, and regulatory implications of AI bots that impersonate humans on social media platforms. As artificial intelligence becomes increasingly integrated into online communication, AI-driven bots are being deployed to mimic human users, influence opinions, and automate engagement. While these technologies offer efficiency, they also raise serious concerns about misinformation, manipulation, transparency, and digital trust. Using a structured online questionnaire distributed via platforms such as Twitter (X), LinkedIn, and WhatsApp, this research gathered responses from 57 participants. The survey examined user perceptions across multiple dimensions, including their confidence in identifying bots, behavioural changes due to bot exposure, ethical concerns, perceived political influence, and expectations for regulation and education. Findings indicate that while many users feel moderately confident in recognizing bots, they also express reduced trust and engagement when bots are suspected. Ethical concerns particularly around privacy and undisclosed AI interaction were prominent, and users widely supported stronger regulation, transparency tools, and public education initiatives. The study concludes that AI bots pose a significant challenge to online authenticity and democratic discourse and highlights the need for multi-stakeholder governance to ensure safe and ethical deployment of such technologies.4 0Item Restricted The Role of Artificial Intelligence in Strengthening Cyber Defense Mechanisms: Opportunities and Challenges(University of Bedfordshire, 2024) Alanazi, Mohammed; Garner, LeeThis study explores the role of Artificial Intelligence (AI) in strengthening cyber defense mechanisms, focusing on the opportunities and challenges it presents. In recent years, AI has shown potential in enhancing threat detection, response efficiency, and proactive cybersecurity measures. The study examines various AI applications in cyber defense, including machine learning for real-time threat identification and natural language processing for analyzing large-scale data patterns. While AI provides significant advantages in mitigating cyber threats, challenges such as model interpretability, ethical concerns, and vulnerability to adversarial attacks persist. The findings contribute to cybersecurity by highlighting both the promising capabilities and limitations of AI in this domain, suggesting future research directions to address these challenges.21 0Item Restricted Trust and Adoption of AI-Powered Cybersecurity in Cloud Computing(Saudi Digital Library, 2025) Algarni, Moneer Mohammed; Baihe, MaThis research investigates the trust and adoption of AI-powered cybersecurity solutions in cloud computing environments. As organizations increasingly rely on cloud services, traditional security approaches fall short in addressing evolving cyber threats. AI-driven tools offer advanced threat detection, anomaly identification, and automated response capabilities. However, concerns about trust, transparency, technical complexity, and data privacy continue to hinder widespread adoption. This study employs a mixed-methods approach, combining surveys and case studies, to explore the key factors influencing trust in AI systems and the barriers to their implementation. The findings highlight the importance of explainable AI, third-party audits, and staff training in building confidence. The research concludes with practical recommendations to help organizations integrate AI into cloud security frameworks effectively.23 0Item Restricted Novel Framework for Integrating Blockchain Technology into Logistics and Supply Chain Services(Saudi Digital Library, 2025) Alkhaldi, Bidah; Al-Omary, AlauddinBottlenecks and operational inefficiencies in supply chains still persist despite technological innovations, due to structural and managerial issues. Blockchain integration presents a viable solution to these long-standing issues by offering tamper resistant ledgers, secure transactions and automation capabilities. This research takes a novel approach by designing a blockchain integration framework for supply chains, modifying the MOHBSChain framework to create the Supply-Blockchain framework. This framework is validated by developing a functional prototype using Hyperledger Fabric, by considering a port decongestion use case scenario. This research adopted an inductive approach, starting with informal observations of real-world port operations and a targeted literature review to identify patterns and challenges. The framework development was guided by the principles of transaction cost economics, resource-based view, and diffusion of innovations theories. MoSCoW method was used to prioritize features, while agile project management was adopted to ensure timely completion. Hyperledger Firefly and its connector framework were used as the middleware to facilitate blockchain integration, while chaincode developed using Go language was packaged and deployed to implement smart contracts. Raft orderer consensus mechanism was chosen to ensure resilience and fault tolerance. From a core functionality standpoint, the prototype allows initiation of smart contracts corresponding to functions such as creating and editing supply chain process-related documents, minimizing manual interventions and enhancing efficiency to reduce port congestion. It also offers live tracking of blockchain transactions, facilitating transparency and oversight, the permissioned nature of Hyperledger Fabric ensures security and robust access controls. Results of functional and performance testing conducted using Hyperledger Caliper, Prometheus, and Grafana, were satisfactory; this indicates the prototype's potential in alleviating bottlenecks in supply chains and quickly delivering benefits to key stakeholders such as port authorities, customs officers, shipping line representatives and logistics providers. In terms of limitations, the prototype is limited to basic functionalities and lacks advanced features required to meet operational and regulatory standards. Future improvements can focus on integrating AI for tasks such as predictive analytics and automated document verification, while technologies such as NFT-based schemas can enhance ownership verification and improve asset tracking.39 0Item Restricted DOES AI INTEGRATION MODERATE THE RELATIONSHIP BETWEEN FIRM GROWTH AND PERFORMANCE IN SMES: THE INFLUENCE OF DECISION-MAKING AND OPERATIONAL PERFORMANCE(University of South Alabama, 2025-05) AlQahtani, Dalal T; Butler, Frank C; Gillis, William E; Hair Jr, Joe F; Scott, Justin TToday’s dynamic business environment requires small and medium-sized enterprises (SMEs) to keep up with technological advancements in order to remain competitive. Business growth creates more challenges for SMEs since they possess fewer available resources than big organizations. Since the introduction of artificial intelligence (AI), several SMEs have been able to compete more effectively and deliver better performance. As part of this research, I examine the possibility that AI integration (AII) will moderate the relationship between firm growth and both decision-making and operational performance, ultimately affecting the performance of SMEs. The aim of this research is to provide practical implications for AI as a strategic resource for improving decision-making capabilities, performance and growth by utilizing the resource-based view (RBV) and information processing theory (IPT). A partial least squares structural equation model (PLS-SEM) was used to analyze data from 338 SME business strategy decision-makers in the United States. In order to verify the measurement model’s reliability and validity, a Confirmatory Composite Analysis (CCA) was performed, followed by the evaluation of the structural model in order to test the hypotheses. In contrast to initial hypotheses, this study found that firm growth is positively related to both decision-making and operational performance. Nevertheless, the study results support the original hypothesis that both decision-making performance (DMP) and operational performance (OPP) positively affect a firm’s performance. Furthermore, AII significantly moderated the relationship between FG and OPP, while it did not significantly moderate the relationship between FG and DMP. This indicates the complexity of the role AI integration plays in SMEs. The paper concludes with recommendations for future research, as well as guidance for practitioners regarding how SMEs can improve their decision-making capabilities and performance using AI.29 0Item Restricted ENHANCING SAFETY AND EFFICIENCY FOR CONTROL SYSTEMS BASED ON REINFORCEMENT LEARNING(The University of Arizona, 2025) Alanazi, Almutasim Billa Abdullah Alanazi; Tharp, Hal StanleyThis dissertation explores applying Artificial Intelligence (AI) techniques, specifically Reinforcement Learning (RL), to develop new control strategies that enhance safety and efficiency across various control systems. RL, recognized for its ability to adapt in dynamic environments without precise knowledge of system dynamics, offers a promising alternative to traditional (classical) control methods, such as Proportional-Integral-Derivative (PID) control. In this research, RL models are applied to four different applications to assess the efficacy of RL in terms of safety and efficiency, namely hydroponic systems, navigation systems, power management, and autonomous vehicles; all RL models are based on model-free RL method, specifically Q-learning and policy optimization algorithms (PPO) approach. The reason for selecting the applications is that safety and efficiency are important keys to such applications. Thus, for our study, we examine the safety and efficiency of model performance and output objectives more closely and note the advantages introduced by the RL models. For the first application, the RL model in the hydroponics system outperforms traditional methods in maintaining optimal pH and Electrical Conductivity (EC) levels, achieving accuracies of 96% and 99%, respectively, and demonstrating reduced settling times under disturbance conditions. In a high-disturbance study over 1,000 episodes, on average, the RL model improved safety by up to 15% by monitoring the system's success and observing the return rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Also, it achieved 35-40% energy savings by enabling the idle mode when the controlled reference was within the desired operating zone. Highlighting its potential to enhance agriculture technologies in terms of safety and efficiency. For the second application, the RL model enhances the navigation system's path planning by incorporating environmental factors when navigating through risk zones, such as poor weather conditions or sandstorms. The strategy involves updating the reward matrix based on safety and efficiency specifications to generate optimal routes that prioritize driver safety. Further improvements are achieved by minimizing energy consumption through these optimal routes, thereby enhancing overall efficiency. For the third application, the RL model in the power management controlled hybrid solar system limits grid reliance by up to 11.76% during peak hours, improving safety, enhancing energy stability, and offering a viable alternative to traditional methods like Time-of-Use programs. A financial analysis suggests a 14-year payback period, with projected profits of $8,500 over 25 years, indicating both economic and environmental benefits. In the fourth application, the RL model for autonomous vehicles employs continuous reward functions that demonstrate improved lane-following and obstacle avoidance, thereby enhancing safety. Compared to existing studies using the AWS DeepRacer simulator, this model reduced training time by 50%, resulting in more efficient learning. The continuous reward mechanism is ideal for real-time scenarios, as it provides the agent with enhanced feedback, leading to better performance and more learning efficiency. The findings highlight RL’s ability to address complex control challenges, enhancing safety and efficiency across examined cases, and underline its potential for broader application in control systems and real-world applications.17 0Item Restricted GRAPH-BASED APPROACH: BRIDGING INSIGHTS FROM STRUCTURED AND UNSTRUCTURED DATA(Temple University, 2025) Aljurbua, Rafaa; Obradovic, ZoranGraph-based methodologies provide powerful tools for uncovering intricate relationships and patterns in complex data, enabling the integration of structured and unstructured information for insightful decision-making across diverse domains. Our research focuses on constructing graphs from structured and unstructured data, demonstrating their applications in healthcare and power systems. In healthcare, we examine how social networks influence the attitudes of hemodialysis patients toward kidney transplantation. Using a network-based approach, we investigate how social networks within hemodialysis clinics affect patients' attitudes, contributing to a growing understanding of this dynamic. Our findings emphasize that social networks improve the performance of machine learning models, highlighting the importance of social interactions in clinical settings (Aljurbua et al., 2022). We further introduce Node2VecFuseClassifier, a graph-based model that combines patient interactions with patient characteristics. By comparing problem representations that focus on sociodemographics versus social interactions, we demonstrate that incorporating patient-to-patient and patient-to-staff interactions results in more accurate predictions. This multi-modal analysis, which merges patient experiences with staff expertise, underscores the role of social networks in influencing attitudes toward transplantation (Aljurbua et al., 2024b). In power systems, we explore the impact of severe weather events that lead to power outages, specifically focusing on predicting weather-induced outages three hours in advance at the county level in the Pacific Northwest of the United States. By utilizing a multi-model multiplex network that integrates data from multiple sources including weather, transmission lines, lightning, vegetation, and social media posts from two leading platforms (Twitter and Reddit), we show how multiplex networks offer valuable insights for predicting power outages. This integration of diverse data sources and network-based modeling emphasizes the importance of leveraging multiple perspectives to enhance the understanding and prediction of power disruptions (Aljurbua et al., 2023). We further present HMN-RTS, a hierarchical multiplex network that classifies disruption severity by temporal learning from integrated weather recordings and social media posts. The multiplex network layers of this framework gather information about power outages, weather, lighting, land cover, transmission lines, and social media comments. By incorporating multiplex network layers consisting of data collected over time and across regions, we demonstrate that HMN-RTS significantly improves the accuracy of predicting the duration of weather-related outages. This framework enables grid operators to make more reliable predictions up to 6 hours in advance, supporting early risk assessment and proactive mitigation (Aljurbua et al., 2024a, 2025a). Additionally, we introduce SMN-WVF, a spatiotemporal multiplex network designed to predict the duration of power outages in distribution grids. By integrating network-based approach and multi-modal data across space and time, SMN-WVF offers a novel method for predicting disruption durations in distribution grids, enhancing decision-making and mitigation efforts while highlighting the critical role of network-based approaches in forecasting (Aljurbua et al., 2025b). Overall, our research showcases the potential of graph-based models in tackling complex challenges in both power systems and healthcare. By combining the network-based approach with multi-modal data, we present innovative solutions for predicting power outages and understanding patient attitudes.23 0