SACM - Australia

Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648

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Kem Boi – AI Content Generation System
(Saudi Digital Library, 2026) Ghadeer Waleed Alkhunaizi, Chen-Ling Lin، Ranaweera Mudiyanselage Kushlani Dinuthri Kumari Amunugama and Saketh Reddy Jambula; Chen, David
The KemBoi AI Content Generation System was developed as part of a Griffith University Work Integrated Learning (WIL) project in collaboration with ApyHome Pty Ltd, a Gold Coast-based dessert brand. The project aimed to design and implement an AI-driven platform capable of automating the generation of marketing content, including ideas, scripts, and promotional video outputs, addressing the time-consuming and resource-intensive nature of traditional content creation processes. The proposed solution introduces a structured AI-powered content generation pipeline that transforms user prompts into complete marketing assets through automated stages, including idea generation, script creation, prompt processing, cinematic video generation, and final media assembly. The project adopted the Agile Scrum methodology and was implemented using HTML, CSS, JavaScript, Python, Flask, SQLite, and external AI APIs such as Together AI. Key deliverables included a web-based dashboard for job creation and monitoring, AI-powered modules for script and video generation, structured database storage, content management capabilities, and job-tracking mechanisms. Security and validation controls, including API key protection and input validation, were also incorporated into the system design.
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Integrating Educational Data Mining and Artificial Intelligence to Enhance ICT User Satisfaction and Administrative Efficiency in Saudi Educational Institutions
(Saudi Digital Library, 2026) Almaghrabi, Hamad; Soh, Ben
The integration of Information and Communication Technology (ICT) in educational administration offers transformative opportunities to enhance efficiency and user satisfaction, but also presents significant challenges. Despite the potential of ICT systems to stream- line processes and support data-driven decision-making, their implementation is often hindered by fragmented infrastructures, inconsistent adoption, and limited alignment with user needs. This thesis addresses these challenges through the design and evaluation of the AI-integrated IiCE framework, developed to strengthen ICT adoption and administrative performance in educational institutions. Educational administrative environments are inherently complex, characterised by mul- tidimensional data, dynamic workflows, and overlapping responsibilities that often expose systemic inefficiencies. The proposed IiCE framework leverages predictive analytics and user-centred design principles to generate actionable insights for optimising ICT utilisa- tion. Its key objectives include identifying the determinants of user satisfaction, enhancing decision-making processes, and fostering an organisational culture that supports technolo- gical innovation and acceptance. Employing a mixed-methods research approach, this study investigates current ICT ad- option practices in Saudi educational institutions. Quantitative and qualitative analyses, incorporating stakeholder perceptions and institutional data, were conducted to uncover adoption barriers and performance gaps. Machine learning (ML) models were applied to predict user satisfaction trends, while SHAP (Shapley Additive Explanations) techniques provided interpretability by highlighting the most influential factors affecting adoption. The framework also integrates adaptive training modules, modular deployment strategies, and continuous feedback mechanisms to ensure sustainability and contextual adaptability. Grounded in Saudi Arabia’s Vision 2030 for digital transformation, the evaluation of the IiCE framework demonstrates its ability to enhance administrative workflows, optim- ise resource allocation, and strengthen stakeholder engagement. Expert validation con- firms its effectiveness in mitigating inefficiencies, promoting collaboration, and supporting evidence-based management practices. This research contributes to the fields of educational administration and ICT innova- tion by presenting an adaptable, AI-driven framework that bridges the gap between tech- nological potential and practical implementation. The findings underscore the value of advanced AI techniques in managing ICT complexity, driving user satisfaction, and im- proving institutional efficiency. Future work may extend this framework through real-time analytics, greater model interpretability, and cross-domain applications for broader educational impact
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AI-Based Approaches for Respiratory Disease Detection Using Audio Signals and Imaging Data
(Saudi Digital Library, 2025) Shati, Asmaa; Hassan, Ghulam Mubashar; Datta, Amitava
Respiratory diseases (RDs) remain major global health concerns, typically diagnosed through imaging and auscultation, with cough sounds also offering diagnostic cues. These methods, however, are often subjective and depend on expert interpretation. Advances in machine learning (ML) enable automated RD diagnosis, yet challenges such as limited data, high computational costs, and accessibility gaps persist, underscoring the need for innovative approaches. This thesis proposes a series of novel approaches for automated RD detection, utilizing either cough audio or CXR as input modalities, selected for their availability and affordability. These approaches integrate advanced techniques for segmentation, feature extraction, and subsequent classification, offering practical and cost-effective diagnostic solutions. Extensive evaluation on multiple open-source datasets demonstrates the effectiveness of the proposed approaches across diverse diagnostic contexts.
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Explainable Goal Recognition Systems
(Saudi Digital Library, 2025) Alshehri, Abeer; Vered, Mor
This 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.
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Trust and Adoption of AI-Powered Cybersecurity in Cloud Computing
(Saudi Digital Library, 2025) Algarni, Moneer Mohammed; Baihe, Ma
This 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.
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The Integration of Artificial Intelligence (AI) In Business Operations
(La Trobe University, 2022) Alqahtani, Raed Ayidh; Soh, Ben
This research investigates the integration of Artificial Intelligence (AI) in business operations. AI has become increasingly prevalent in various industries due to its potential to enhance efficiency, improve decision-making, and drive innovation. However, there is a lack of comprehensive understanding of how AI integration has been implemented in the business context. Therefore, this study utilizes a systematic review approach, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, to examine and synthesize existing literature on AI integration in business operations. The primary outcomes of this research will provide insights into the current state of AI integration in businesses, identify common challenges and benefits, and highlight potential areas for future research. This research contributes to the understanding of the impact of AI on business operations, paving the way for the effective and successful implementation of AI in organizations.
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The Role of Artificial Intelligence in Project Management
(University of Technology Sydney, 2024-11-11) Muryif Alshehri, Mohammed; Abdo, Peter
The increasing complexity of global projects has elevated the challenges in project management, necessitating the adoption of innovative solutions. This study investigates the transformative potential of Artificial Intelligence (AI) in project management, emphasizing its role in enhancing decision-making, risk management, and operational efficiency. Employing a systematic literature review methodology, the research synthesizes findings from 13 high-index journal articles to evaluate AI techniques, including machine learning, decision trees, and advanced predictive analytics. The study identifies AI’s ability to improve resource allocation, forecasting accuracy, and stakeholder engagement while mitigating risks and optimizing sustainability. Findings highlight the integration challenges such as data quality, system compatibility, and resistance to change, which hinder the widespread adoption of AI tools. Despite these obstacles, AI demonstrates considerable benefits, including automation of routine tasks, enhanced cost estimation, and improved project timelines. Notably, AI-driven tools have achieved a 20% reduction in project completion times and a 15% decrease in costs due to proactive risk mitigation. This research provides actionable insights into the effective implementation of AI within the framework of traditional project management methodologies. It concludes that while AI presents significant opportunities to redefine project management practices, its successful adoption requires addressing technical and organizational challenges, along with fostering an adaptive cultural mindset. This study lays the groundwork for future research aimed at leveraging AI to create sustainable, efficient, and resilient project management ecosystems.
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AI in Telehealth for Cardiac Care: A Literature Review
(University of technology sydney, 2024-03) Alzahrani, Amwaj; Li, lifu
This literature review investigates the integration of artificial intelligence (AI) in telehealth, with a specific focus on its applications in cardiac care. The review explores how AI enhances remote patient monitoring, facilitates personalized treatment plans, and improves healthcare accessibility for patients with cardiac conditions. AI-driven tools, such as wearable devices and implantable medical devices, have demonstrated significant potential in tracking critical health parameters, enabling timely interventions, and fostering proactive patient care. Additionally, AI-powered chatbots and telehealth platforms provide patients with real-time support and guidance, enhancing engagement and adherence to treatment regimens. The findings reveal that AI contributes to improving healthcare outcomes by enabling early detection of cardiac events, tailoring treatment plans to individual patient needs, and expanding access to care for underserved populations. However, the integration of AI in telehealth is not without challenges. Ethical considerations, such as ensuring data privacy, managing biases in AI algorithms, and addressing regulatory complexities, emerge as critical areas requiring attention. Furthermore, technological limitations, including the need for robust validation and patient acceptance of AI technologies, underscore the importance of bridging the gap between research and real-world implementation. This review also examines future trends, including the integration of blockchain technology with AI to enhance data security and privacy in telehealth systems. Advancements in machine learning and the Internet of Things (IoT) are paving the way for innovative solutions, such as secure remote monitoring and personalized rehabilitation programs. While AI holds transformative potential in revolutionizing telehealth services for cardiac patients, addressing these challenges is imperative to ensure equitable, effective, and patient-centered care. This review underscores the need for interdisciplinary collaboration and regulatory oversight to unlock the full potential of AI in telehealth and improve outcomes for cardiac patients globally.
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Utilizing Artificial Intelligence to Develop Machine Learning Techniques for Enhancing Academic Performance and Education Delivery
(University of Technology Sydney, 2024) Allotaibi, Sultan; Alnajjar, Husam
Artificial Intelligence (AI) and particularly the related sub-discipline of Machine Learning (ML), have impacted many industries, and the education industry is no exception because of its high-level data handling capacities. This paper discusses the various AI technologies coupled with ML models that enhance learners' performance and the delivery of education systems. The research aims to help solve the current problems of the growing need for individualized education interventions arising from student needs, high dropout rates and fluctuating academic performance. AI and ML can then analyze large data sets to recognize students who are at risk academically, gauge course completion and learning retention rates, and suggest interventions to students who may require them. The study occurs in a growing Computer-Enhanced Learning (CED) environment characterized by elearning, blended learning, and intelligent tutelage. These technologies present innovative concepts to enhance administrative procedures, deliver individualized tutorials, and capture students' attention. Using predictive analytics and intelligent tutors, AI tools can bring real-time student data into the classroom so that educators can enhance the yields by reducing dropout rates while increasing performance. Not only does this research illustrate the current hope and promise of AI/ML in the context of education, but it also includes relevant problems that arise in data privacy and ethics, as well as technology equality. To eliminate the social imbalance in its use, the study seeks to build efficient and accountable AI models and architectures to make these available to all students as a foundation of practical education. The students’ ideas also indicate that to prepare the learning environments of schools for further changes, it is necessary to increase the use of AI/ML in learning processes
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Factors Driving Individuals’ Usage Intention of Artificial Intelligence (AI) Assistants in E-commerce: Perspectives of Users and Non-Users
(University of Technology Sydney, 2024) Alnefaie, Ahlam Eid Awad; Kang, Kyeong; Sohaib, Osama
The ongoing revolution of e-commerce has brought about significant transformations in the global retail landscape, redefining how consumers interact with online platforms. In response to this transformative trend, businesses increasingly adopt and integrate artificial intelligence (AI) technologies, particularly AI assistants. AI assistants have gained significant traction to enhance customer engagement, improve personalised experience, and streamline various aspects of the e-commerce process. Companies across diverse industries and geographical regions have recognised the potential of AI assistants in fostering more profound connections with customers, providing real-time support, and bolstering sales through intelligent recommendations. Consequently, investment in AI research and development has surged, leading to remarkable advancements in AI assistants’ features and functionalities. Despite the growing interest of the scientific community and business stakeholders in the topic, scholarly research on the factors influencing e-commerce consumers’ attitudes and intentions toward using AI assistants is still limited and provides contradictory evidence regarding some factors. Moreover, no comparative studies in the e-commerce context empirically investigated the attitudes of non-users and users toward AI assistant use. Also, several consumers' demographics have been excluded from prior research, with no previous empirical research on AI assistant use across different cultural backgrounds. For these reasons, the study aimed to comprehend the factors influencing consumers' behavioural intention to utilise AI assistants and to recognise the significant user differences based on multiple perspectives. This study employed a unique research model based on the technology acceptance model. It extended it with external factors of AI assistants’ capabilities that still need to be tested together in AI assistant adoption for e-commerce consumers. This research conducted a mixed-method approach. In the first phase (Phase A), a quantitative method was employed to investigate the relationships between the constructs in the study model, and the Partial Least Square Structural Equation Modelling (PLS-SEM) and several statistical techniques were adopted. Furthermore, to account for cross-cultural differences and identify potential variations in usage intentions towards using AI assistants between Eastern and Western cultures, a multi-group analysis (MGA) was conducted. In the second phase (Phase B), a qualitative approach was conducted by applying machine learning and natural language processing techniques to analyse reviews of the Louis Vuitton brand's e-commerce applications. The objective of this stage was to obtain supporting evidence for the results of the VI first study and to gain deeper insights into consumer attitudes and experiences. Subsequently, the results were integrated to provide multiple insights to answer the research questions and strengthen the findings. This study has confirmed some previous studies' results and provided new findings. The attitude factor was the significant predictor of the intention to use AI assistants in non-users and users, with a direct and positive effect. Perceived usefulness was found to be the statistically significant predictor of attitudes in both non-users and users of AI assistants. The additions to the original TAM model, specifically incorporating interactive communication and personalisation, were statistically significant predictors of the attitudes of non-users and users to use AI assistants with positive effects. In contrast, perceived ease of use was a nonsignificant predictor of the non-users’ attitudes and positively impacted the users’ attitudes towards using AI assistants. Furthermore, no significant differences existed in the relationships among the primary factors influencing the intention to utilise AI assistants in e-commerce when comparing Western and Eastern cultural groups. This study contributes to both theory and practice by extending the TAM model with two external factors enabling the assessment of the factors affecting the intention to use AI assistants from consumer, social, and marketing perspectives and providing new empirical data on this topic in technology adoption studies. The study also enables further research on this topic and comparing study results, thus improving understanding of the phenomenon. It also provides various e-commerce practitioners with valuable information and recommendations regarding AI assistant use, enabling them to make better decisions in developing and implementing AI assistant technologies.
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