SACM - United States of America

Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668

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Modeling Learners’ Cognition and Metacognition In Data Science Problem Solving
(Saudi Digital Library, 2026) Alomair, Maryam M; Shimei, Pan; Lujie, Karen Chen
In 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.
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Using Artificial Intelligence (AI) to Improve the Identification of Gifted Students
(Saudi Digital Library, 2025) Almalki, Hamid Ahmad; Beck, Dennis E
The 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.
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AI-Enabled Autonomous Knowledge Extraction from Large-Scale Textual Data
(Saudi Digital Library, 2026) Alharbi, Abdulrahman; Obradovic, Zoran
The 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.
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ENHANCING TRAFFIC SAFETY THROUGH AI-DRIVEN, PRIVACY-PRESERVING, AND SECURE IMPAIRED DRIVING DETECTION SYSTEMS
(Saudi Digital Library, 2026) Alsulieman, Razan; Sherif, Ahmed
Drunk driving remains a major threat to road safety worldwide, contributing significantly to traﬃc injuries and fatalities each year. Traditional detection approaches are largely reactive and vehicle-centric, relying on in-vehicle sensors, breathalyzers, or post-incident enforcement. These methods often depend on driver cooperation, intrusive hardware installations, or limited monitoring environments, restricting their scalability and eﬀectiveness in large transportation systems. At the same time, modern cities increasingly deploy roadside cameras, surveillance networks, and drone based monitoring systems, creating new opportunities for proactive intoxication detection at the infrastructure level. However, leveraging such external monitoring introduces challenges related to secure data collection, reliable AI-based analysis, privacy protection, and real-world deployment. This dissertation proposes a secure, privacy-preserving Artificial Intelligence framework for proactive drunk driving detection using out-of-vehicle surveillance data. The framework addresses three key aspects required for reliable infrastructure-level monitoring. First, a lightweight authentication scheme is developed to ensure secure data collection from distributed monitoring platforms such as drones and surveillance devices. The proposed design employs physically unclonable functions and symmetric cryptographic primitives to provide protection against impersonation, replay attacks, and device cloning while maintaining low computational overhead for resource-constrainedenvironments. Second, AI-based intoxication detection models are developed using Machine Learning and Deep Learning techniques to analyze facial imagery captured under real-world surveillance conditions. Extensive experiments evaluate multiple models under varying noise and disruption scenarios to ensure robustness across both low- and high-resource computational environments. The framework also incorporates explainable AI methods to improve transparency and verify that model decisions rely on meaningful facial features. Finally, the framework integrates privacy-preserving learning mechanisms through federated learning, enabling distributed model training without transferring sensitive facial images to centralized servers. This approach protects user privacy while maintaining strong detection performance across distributed monitoring nodes. These contributions establish a secure, scalable, and privacy-aware infrastructure-level system for proactive intoxication detection, supporting intelligent transportation systems aimed at improving traﬃc safety.
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A Novel Emoji-Aware Computational Framework for Body-Shaming Detection in Gulf Arabic Social Media Discourse
(Saudi Digital Library, 2026) Albluwi, Abeer; Rizk, Dominick
Online body-shaming has become a prevalent form of appearance-based harm in Gulf Arabic social media, particularly on TikTok and Instagram. In these environments, harmful content is rarely explicit. It is carried through indirect and culturally embedded forms of expression, including sarcastic religious expressions and emojis whose meaning is often context-dependent and inverted. Despite the scale of the problem, no prior Arabic NLP work had addressed body-shaming as a standalone classification task, and no dataset existed to support it. This dissertation introduces GABSD-E (Gulf Arabic Body-Shaming Dataset, Emoji-Enriched), the first annotated corpus built specifically for this task. The dataset contains 24,988 comments from TikTok and Instagram, labeled under a three-class taxonomy: Body-Shaming (BS), General Bullying (B), and Not Bullying (NB), with Fleiss' kappa = 0.87. Exploratory analysis confirmed that 42.9% of comments contain at least one emoji, and that the BS class has the highest emoji density relative to class size, establishing that emojis are structurally embedded in how body-shaming is communicated, not incidental to it. Building on this, the dissertation proposes a novel emoji-aware representation framework that treats emojis as culturally grounded semiotic units rather than preprocessing noise. The framework consists of three components: a semantic emoji tagging layer based on a manually constructed Gulf Arabic emoji dictionary; a contextual lexicon injection layer for culturally specific expressions; and a preservation-over-deletion preprocessing pipeline that reverses the standard Arabic NLP practice of emoji removal. Five Arabic transformer models were fine-tuned and evaluated under this framework. SaudiBERT achieved the best performance, with a mean Macro-F1 of 0.9519 ± 0.0030 and accuracy of 95.27% ± 0.30 across five random seeds, substantially above all classical baselines (best classical Macro-F1: 0.6906).Ablation results confirmed that emoji removal caused the largest per-condition performance drop (−0.0589 Macro-F1), with consistent degradation observed across all classes, indicating that emoji signals provide complementary contextual cues that improve discrimination across categories. Semantic enrichment was the most critical component for the BS class (ΔF1-BS = +0.0775). An exploratory benchmark of four large language models showed that the best LLM under few-shot prompting (GPT-4o, Macro-F1 = 0.9278) still fell 0.0279 points below the fine-tuned SaudiBERT, confirming that model scale alone does not substitute for culturally grounded domain-specific modeling. The dissertation contributes GABSD-E as a reusable dataset and benchmark framework, an annotation methodology applicable to related harm categories, and a representation framework that generalizes to any Arabic NLP task where emoji pragmatics carry discriminative weight.
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A Facial Expression-Aware Edge AI System For Driver Safety Monitoring
(Saudi Digital Library, 2025) Almodhwahi, Maram; Wang, Bin
This dissertation presents a driver monitoring system (DMS) that integrates emotion recognition to address critical issues in road safety. Road safety has become a global concern due to the significant increase in vehicle numbers and the rapid growth of transportation infrastructure. The number one cause of road accidents is human error, with a 90% ratio, with common contributing factors like distraction, drowsiness, panic, and fatigue. Traditional DMS approaches often fall short in identifying these emotional and cognitive states, limiting their effectiveness in accident prevention. To address these limitations, this research proposes a robust, deep-learning-based DMS framework designed to identify and respond to driver emotions and behaviors that may compromise safety. The proposed system utilizes advanced convolutional neural networks (CNN), specifically the inception module and Caffe-based ResNet-10 with a single-shot detector (SSD), to perform efficient facial detection and classification. These chosen model structures helped balance computational efficiency and accuracy. The DMS is trained on an extensive, diverse dataset comprising approximately 198,000 images and 1,600 videos sourced from multiple public and private datasets, ensuring the system’s robustness across a range of emotions and real-world driving scenarios. Emotions of interest include high-risk states such as drowsiness, distraction, and fear, alongside neutral conditions, and the model can perform well in different conditions, including low-light and foggy/blurry environments. Methodologically, the system incorporates essential data preprocessing techniques such as resizing, brightness normalization, pixel scaling, and noise reduction to optimize the model’s performance. On top of that, data augmentation and grayscale conversion improves the dataset’s variability, allowing the decrease of computational costs without sacrificing accuracy. This approach enabled the model to achieve high performance metrics, with an overall accuracy of 98.6% , an F1-score of 0.979, precision of 0.980, and recall of 0.979 across the four primary emotional states. This research contributes to the field by offering a less invasive, real-time solution for monitoring high-risk driver behaviors and providing insights for further advancements in automated driver assistance technologies. Future directions include optimizing the system for microcontrollers with low power consumption and implementing alerts for high-risk states to further mitigate accident risks, as well as including a multi-modal fusion of data from different sources (Infrared Camera, and a Microphone) to increase emotion recognition accuracy, which leads to taking better control and initiating more efficient proactive interventions.
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Artificial Intelligence, Deep Learning, and the Black Box Opacity: International Law and Modern Governance Framework for Legal Compliance and Individual Responsibility
(Saudi Digital Library, 2025) Aloqayli, Muhannad Khalid; Linarelli, John
This dissertation examines the unprecedented challenges that deep learning models in artificial intelligence pose to international humanitarian law frameworks governing armed conflict, addressing critical questions about international humanitarian law compliance capabilities, legal personality under the framework of international law and international humanitarian law, and international individual criminal responsibility when autonomous weapons systems employ deep learning models in decision-making processes. Chapter Two provides a comprehensive technical analysis of deep learning architectures, including convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformer networks, and their military applications in target recognition, threat assessment, and autonomous operations. The analysis demonstrates that properly trained deep learning systems can achieve exceptional accuracy in tasks relevant to the principles of distinction and proportionality. However, this technical capability exists alongside a fundamental limitation: the “black box challenge,” whereby decision-making processes emerge from statistical pattern recognition across billions of parameters in ways that remain incomprehensible to human operators, creating unprecedented challenges for legal compliance and individual responsibility. Chapter Three evaluates whether granting legal personality to advanced artificial intelligence could address emerging responsibility gaps. Applying the analytical pragmatic approach through dual criteria of “value context” and “legitimacy context,” the analysis reaches definitive negative conclusions. Granting artificial intelligence legal personality would contradict international humanitarian law’s human-centered foundations, fail to fill responsibility gaps, and potentially shield humans from liability while introducing conceptual incoherence into established normative structures. Chapter Four demonstrates that deep learning, as a black box model in statistical learning, fundamentally challenges traditional international frameworks for individual criminal responsibility. The analysis reveals structural incompatibilities between algorithmic opacity and the requirements of the Rome Statute for mens rea and actus reus. Similarly, command responsibility doctrines face parallel challenges when commanders possess formal control over systems whose decision-making processes transcend human comprehension. The dissertation proposes a modified command responsibility framework recognizing commanders as “AI enablers” rather than traditional superiors, establishing reasonable governance standards for controlled environments while imposing strict liability for high-risk deployments. This framework preserves meaningful accountability while acknowledging technological constraints, shifting focus from comprehending opaque statistical processes to governing deployment decisions and operational contexts within commanders’ control.
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How Large Language Models are Reshaping Skills and Job Requirements for Public Health Professionals in Saudi Arabia
(Saudi Digital Library, 2025) Alkhinjar, Mulfi; Palmer, Paula
Context: 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.
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ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE TEACHING AND LEARNING: LEVERAGING AI FOR LEARNER SUPPORT AND TEACHER DEVELOPMENT
(Saudi Digital Library, 2025) Alyobi, Mazen; Egbert, Joy
This 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.
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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 T
Today’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.
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