Saudi Cultural Missions Theses & Dissertations
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Item Restricted Teacher's Perceptions of AI Tools in English Academic Writing Instruction(Saudi Digital Library, 2026) Alotaibi, Najla; Zhang, TerrenceThis research examines university-level instructors' perceptions of generative artificial intelligence (AI) applications, such as ChatGPT, in teaching academic writing. The study explores instructors' perceptions, interpretations, and adaptations regarding the challenges and opportunities of AI integration in their classes. This research relies on semi-structured interviews to examine how teachers use AI in their writing classes, their concerns about originality and academic integrity, and how they are adjusting their pedagogical practices to incorporate AI. The findings show that although teachers believe AI can enhance students' learning, they are also concerned about overreliance on technology and its impact on students' critical thinking. The study provides insights into the evolving role of AI in writing pedagogy and offers practical implications for incorporating AI into educational institutions without compromising academic integrity.11 0Item Restricted How Large Language Models are Reshaping Skills and Job Requirements for Public Health Professionals in Saudi Arabia(Saudi Digital Library, 2025) Alkhinjar, Mulfi; Palmer, PaulaContext: 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.18 0Item Restricted Evaluating the Accuracy and Reliability of ChatGPT-4o Mini in Generating Academic References: Impact of Prompt Engineering and Comparative Analysis of Direct Questions vs. Guided Questions Approaches(Saudi Digital Library, 2025) Alqahtani, Asyah; Xavier, CarpentThe rapid development of large language models (LLMs) such as ChatGPT attracts the attention of researchers and academics due to its advanced capabilities. However, this comes with controversy on whether it generates authentic academic references. Therefore, this dissertation investigates how use various prompt engineering techniques affect the accuracy and reliability of academic references that generated by ChatGPT-4o mini, by examining whether these references exist in academic databases. The academic disciplines included in this study: computer science, electrical engineering, biology, history, medicine, psychology, and geography. Two approaches are employed to ask the model: direct questions (without prompt engineering) and guided questions (using prompt engineering). A total of 700 questions are analyses, with 100 questions per discipline equally divided between the two approaches. The generation academic references are then checked using the CrossRef, Scopus, and OpenLibrary databases. The findings show differences in the performance of the model across disciplines with the use of prompt engineering techniques. The enhancements in the accuracy of the generated academic references vary, with a 10.75% increase observed in biology and a 2.48% increase in medicine. Conversely, psychology suffers a little decline of 1.08% in accuracy, and electrical engineering faces a significant drop of 8.42% point. These variations show how specific questioning techniques can improve the generated academic references accuracy in some fields yet prove less effective in other fields. The dissertation also examines non-existence academic references generated by ChatGPT-4o mini, discovering that 5% to 16% of these fake academic references include authentic author identities. It shows the model sometimes correctly associates writers with their relevant fields, but frequently connects them to other areas not relevant fields. Nevertheless, fake references with fake authors remain more prevalent. These findings reveal considerable difficulties in using LLMs for the generation of authentic academic references regards to accuracy and reliability. Although prompt engineering techniques have demonstrated improvements in certain domains, the total incidence of fake academic references remains substantial, this highlights the importance of human verification.16 0Item Restricted EFL SAUDI UNIVERSITY LEARNERS' PERCEPTIONS AND USE OF GENERATIVE AI SOFTWARE IN LANGUAGE LEARNING(The University of Memphis, 2025) Almohammadi, Asaad Hamed; Thrush, EmilyAs generative artificial intelligence (AI) tools become increasingly integrated into educational ecosystems, understanding how learners interact with these technologies is crucial, particularly in underrepresented English as a Foreign Language (EFL) contexts. This dissertation investigates the perceptions and usage patterns of Saudi university students as they interact with generative AI software to support their English language learning. While AI technologies like ChatGPT, Grammarly, and QuillBot are rapidly transforming academic practices, limited research has explored their impact from the learner’s perspective in non-Western, non-native English-speaking environments. This mixed-methods study, grounded in the Technology Acceptance Model (TAM), was conducted at one of the largest educational institutions in Saudi Arabia. The quantitative phase included 317 valid student responses analyzed using JASP. Principal Component Analysis confirmed the TAM-based factor structure, and repeated-measures ANOVA tested for differences across demographic variables such as gender, academic level, and years of English study. Findings indicated generally favorable perceptions of generative AI tools, with perceived ease of use rated higher than perceived usefulness. While no major gender-based differences were observed, modest distinctions emerged across academic levels and language study history. To contextualize these patterns, the qualitative phase involved 17 semi-structured interviews analyzed thematically using Quirkos. The qualitative findings provided deeper insight into how students incorporated AI tools into their learning routines across reading, writing, speaking, and listening tasks. Themes included increased learner autonomy, more personalized and immediate feedback, enhanced motivation, and flexible pacing. However, students also raised concerns about the accuracy of the tool, its insensitivity to context, the lack of critical reflection, and the risk of over-reliance on automated feedback. These nuanced findings were presented using bilingual excerpts (Arabic and English) to preserve cultural and linguistic authenticity. Together, the mixed-methods results suggest that generative AI tools offer both pedagogical opportunities and challenges. While learners recognize the value of these tools for improving task efficiency and engagement, they also navigate tensions between convenience and cognitive growth. Overuse of AI software, particularly for writing, may inhibit deeper learning and reduce confidence in independent language production. This dissertation contributes to the evolving body of research on AI in education by centering learner voices in a global context. It advances the theoretical application of TAM in EFL environments and underscores the need for culturally responsive, pedagogically guided integration of AI tools. The findings hold implications for curriculum designers, instructors, and edtech developers seeking to balance innovation with integrity in technology-enhanced language learning.Item Restricted The Impact of ChatGPT Use on the Motivation and Basic Psychological Needs of Saudi EFL Students(Arizona State University, 2025) Alwadai, Abdullah; Smith, BryanThe advent of generative artificial intelligence (GenAI) represents a significant technological breakthrough, enabling models like ChatGPT to facilitate human-like interactions, personalized learning, constructive feedback, and so on. The sophistication of generative AI has attracted considerable attention from researchers who explore its potential benefits for language learners. However, since it was released a few years ago, research on ChatGPT’s effectiveness in enhancing language learning motivation for English as a foreign language (EFL) learners remains limited. To this end, this research utilized the self-determination theory (SDT) to investigate the extent to which engaging in informal interactions with ChatGPT in extramural contexts influences motivation and the basic psychological needs for autonomy, competence, and relatedness. To achieve this, a quasi-experimental design was employed with fifty EFL Saudi undergraduate students majoring in English, divided equally into an experimental and a control group. The experimental group participated in nine sessions over three weeks, engaging in informal self-directed interactions with ChatGPT on common everyday topics, while the control group responded to the same topics in writing without using AI, following the same number of sessions. The post-test results analyzed using analysis of covariance (ANCOVA) indicated a substantial increase in autonomous motivation and a decrease in controlled motivation in the experimental group after the treatment. Moreover, the results revealed a significant increase in autonomy exclusively in the experimental group. However, no statistically significant difference was observed in competence and relatedness after the intervention. Informed by these findings, a number of implications and pedagogical recommendations for language instructors, policymakers, and other stakeholders were proposed.Item Restricted Investigating and Enhancing Online Software Development Resources: Automated Responses, Semantic Search, and Tagging in Video Tutorials(Florida State University, 2024) Tayeb, Ahmad; Haiduc, SoniaThe field of software development is rapidly evolving, requiring developers to continually refine their skills and adapt to new technologies. While video tutorials have become a popular medium for learning new concepts and techniques, challenges persist in interactive engagement, search functionality, and effective tagging. This dissertation explores innovative methods to enhance software development video tutorials by addressing these challenges using advanced large language models and transformer-based models. Firstly, we explore developers' preferences in terms of online learning resources in the era of AI-driven chatbots like ChatGPT. Despite the rise of AI chatbots, which offer instant, personalized responses, video tutorials remain a preferred medium due to their visual and detailed explanations. However, our study also revealed opportunities for improving video tutorials by integrating interactive elements and leveraging AI technologies, setting the foundation for our subsequent projects. Building on these insights, we introduce VidTutorAssistant, a system that leverages Generative Pre-trained Transformer (GPT) models to automate responses to video tutorial viewers' questions, thereby increasing interactive engagement in video tutorials and enhancing the learning experience. Next, we present an improved video tutorial search method, ISM, that leverages transformer-based models to create semantically dense vectors from video data, enabling a more intuitive and efficient search experience. By capturing the contextual meaning of queries and video content, ISM surpasses traditional search methods, helping developers find the most relevant tutorials and specific content within them. Finally, we introduce BM25-BERT, a hybrid approach for refining video tagging that combines traditional BM25F methods with transformer-based models. By re-ranking candidate tags initially generated by BM25F to improve context-awareness and accuracy, this method significantly enhances tutorial discoverability. Through empirical studies and user evaluations, this dissertation advances software engineering and educational technology by offering innovative solutions to enhance video tutorials and examining the impact of AI tools on developers' learning preferences. By rigorously assessing the proposed methodologies, this research contributes to both academic research and practical applications.Item Restricted Exploring the Efficacy of ChatGPT Feedback in L2 Writing: Perspectives from Teachers and Students(Lancaster University, 2024) Alamri, Tahani; Lo, JustinThe emergence of user-friendly, widely accessible generative AI tools like ChatGPT has sparked widespread interest in academia. As students increasingly turn to these tools for various tasks, educational institutions have adopted different policies, from complete bans to active exploration of their potential applications. One frequently proposed application in recent research is using these tools to provide written corrective feedback on students' writing. This dissertation investigates the efficacy of ChatGPT feedback and the potential of AI tools in L2 writing instruction through the perspectives of L2 teachers and students. The aim is to contribute to the current understanding of AI and how it could potentially benefit or hinder L2 writing instruction, as well as determine the optimal approach for incorporating this tool. To achieve this, two online questionnaires were utilized, one targeting teachers and one for students. Participants evaluated samples of GPT feedback on a 5-criteria scale and shared their insights about the potential role of GPT in writing instruction in addition to the potential benefits and concerns. The findings showed that teachers and students generally viewed GPT feedback favorably. However, they acknowledged the limitations of AI feedback and the potential challenges AI systems might create if misused. Therefore, the majority opted for a balanced approach between teacher and AI feedback for maximized benefits and reducing risks.Item Restricted Saudi Arabian Students’ Perceptions of Chat GPT in Relation to their English Language Writing Skills while Studying in the UK(Newcastle University., 2024-08-30) Alsubaie, Jawaher Obaid; Seedhouse, PaulThis research investigates Saudi Arabian students' perceptions of ChatGPT and its impact on their English writing skills while studying in the UK. The study aims to explore how ChatGPT influences Saudi students’ writing development, uncover the perceived advantages and disadvantages of the tool, and provide recommendations for its integration into Saudi education in alignment with AI policies. A mixed-method research design was employed, incorporating both quantitative and qualitative approaches. Data were collected through a questionnaire administered to 270 Saudi EFL students in the UK, documents analysis, and semi-structured interviews. The findings reveal that Saudi students generally perceive ChatGPT as a beneficial tool for improving their English writing skills, particularly in terms of technical writing, idea generation, and increased confidence. However, concerns were raised about over-reliance on the tool and inaccuracies in its output. The qualitative interviews confirmed these results, shedding light on how students view ChatGPT as a supportive resource for enhancing language proficiency and efficiency, while also noting the potential for dependency and misinformation. Moreover, the analysis shows both Newcastle university and Saudi AI policies prioritize responsible AI and academic integrity but differ in focus. Saudi policies emphasise risk management, while Newcastle prioritizes academic integrity and learning. Sentiment analysis revealed Saudi's cautious approach, while Newcastle balances risks with the benefits of AI. Based on these findings, the study recommends integrating ChatGPT into Saudi educational settings with clear guidelines, proper training, and an emphasis on responsible usage. Such measures will help ensure that students can maximize the benefits of AI tools while mitigating the risks, particularly as they advance in their academic and professional careers.Item Restricted EXAMINING SAUDI ARABIAN'S PERCEPTIONS AND ADOPTION OF CHATGPT(Texas Tech University, 2024-05-10) Huda, Asiri; Xu, ShanThis paper explores the compatibility, benefits, and adoption rate of ChatGPT among Saudi Arabian populations, recognizing the nation’s unique cultural landscape. As AI technologies like ChatGPT redefine human-tech interactions globally, understanding their reception in diverse cultural contexts is imperative. Through qualitative and quantitative analysis, this study elucidates Saudi perceptions and usage patterns of ChatGPT, offering insights for businesses, policymakers, and developers to align AI innovations with societal values and preferences effectively. This research paper seeks to address the perception and adoption of ChatGPT among Saudi Arabian individuals. The importance of this study lies in the potential to bridge the gap between cutting-edge technology and the unique cultural and societal dynamics of the Saudi Arabian populace. By analyzing the perceptions and experiences of Saudi Arabian users, we can identify areas where ChatGPT can be harnessed to enhance communication. When the needs of Saudi Arabian users are identified, it can lead to integration of AI into their daily lives and contributing to the ongoing technological transformation in the region. The three theories that can be used to understand ChatGPT adoption in Saudi Arabia are Diffusion of Innovations Theory and Cultural Dimension TheoryItem Restricted Creating Synthetic Data for Stance Detection Tasks using Large Language Models(Cardiff University, 2023-09-11) Alsemairi, Alhanouf; Manchego, Fernando AlvaStance detection is a natural language processing (NLP) task that analyses people’s stances (e.g. in favour, against or neutral) towards a specific topic. It is usually tackled using supervised classification approaches. However, collecting datasets with suitable human annotations is a resource-expensive process. The impressive capability of large language models (LLMs) in generating human-like text has revolutionized various NLP tasks. Therefore, in this dissertation, we investigate the capabilities of LLMs, specifically ChatGPT and Falcon, as a potential solution to create synthetic data that may address the data scarcity problem in stance detection tasks, and observe its impact on the performance of stance detection models. The study was conducted across various topics (e.g. Feminism, Covid-19) and two languages (English and Arabic). Different prompting approaches were employed to guide these LLMs in generating artificial data that is similar to real-world data. The results demonstrate a range of capabilities and limitations of LLMs for this use case. ChatGPT’s ethical guidelines affect its performance in simulating real-world tweets. Conversely, the open-source Falcon model’s performance in resembling the original data was better than ChatGPT’s; however, it could not create good Arabic tweets compared to ChatGPT. The study concludes that the current abilities of ChatGPT and Falcon are insufficient to generate diverse synthetic tweets. Thus, additional improvements are required to bridge the gap between synthesized and real-world data to enhance the performance of stance detection models.