SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted The ethical implications of using Multimodal Learning Analytics: a framework for research and practice(University College London (UCL), 2025) Alwahaby, Hifa; Cukurova, MutluA growing number of multimodal data (MMD) streams and complex artificial intelligence (AI) models are being used in learning analytics research to allow us to better understand, model and support learning, together with teaching processes. Considering MMDs’ potentially more invasive, extremely granular and temporal nature compared to log files, they may present additional ethical challenges in comparison to more traditional learning activity data. The systematic review undertaken during this study revealed a dearth of ethical considerations in previous multimodal learning analytics (MMLA) literature. Consequently, this study aims to identify the ethical issues associated with the use of MMLA and propose a practical framework to assist end-users to become more aware of these issues and potentially mitigate them. To gain a better understanding of the ethical issues and how they may be mitigated, the study aims to investigate the ethical concerns associated with the use of MMLA in higher education by collecting the opinions and experiences of appropriate stakeholders. Accordingly, structured individual interviews were conducted via Microsoft Teams, a video conferencing software, due to COVID-19 restrictions. In total, 60 interviews were conducted with educational stakeholders (39 higher education students, 12 researchers, eight educators and one representative of an educational technology company). Based on the thematic coding of verbatim transcriptions, nine distinct themes were identified. In response to the themes and accompanying probing questions presented to the MMLA stakeholders, and based on the ethical guidance and recommendations identified from previous literature, a first draft of the MMLA ethical framework was prepared. Subsequently, the draft was evaluated by 27 evaluators (seven higher-education students, 13 researchers–practitioners, four teachers, one ethics expert and two policymakers) by means of structured interviews. Additionally, a group of researchers adopted the framework in their research and provided constructive feedback. Based on the thematic analysis of the interviews, the framework was continually improved for three rounds until data saturation was achieved. This resulted in the presentation of the first MMLA ethical framework, which was the principal goal of this study. This thesis delivers three key contributions: (1) a systematic review of previous MMLA literature that confirms the lack of ethical considerations in the literature; (2) an examination of the ethical issues connected with MMLA from the perspective of different stakeholders; and (3) an ethical MMLA framework for higher education. By developing the framework, this thesis aims to increase awareness of the potential ethical issues and therefore, alleviate them by promoting a more ethical design, along with the development and use of MMLA in a higher education setting.3 0Item Restricted PerfectHR: Using AI to Reduce Candidate-Job Mismatch and Improve Recruitment Efficiency(Queen Mary University of London, 2025) Baraheem, Ghadeer; Wijetunge, PiyajithThe recruitment process is critical for organizations to find the right talent. However, existing recruitment software often faces issues like candidate-job mismatches and biases, leading to inefficient hiring processes. This paper presents PerfectHR, a recruitment software solution designed to reduce candidate-job mismatches and improve recruitment efficiency using artificial intelligence. The software integrates a logistic regression model for candidate classification and OpenAI’s GPT-4 language model for CV summarization. PerfectHR addresses bias in the dataset and algorithm by excluding sensitive features such as age and gender to ensure that they do not influence the model predictions. The application was developed using React.js for the frontend, Node.js for the backend, MongoDB for database management, and deployed on Vercel. Initial testing indicates that PerfectHR provides a reliable and user-friendly experience, effectively supporting job postings, candidate evaluations, and communication. Future work will focus on expanding the training dataset to cover a broader range of job types and further refining the application to improve performance and scalability.24 0Item Restricted Sharper Swords, Tougher Shields The Impact of GenAI on the Offensive-Defensive Balance in Cyberspace(King’s College London, 2024-08-26) Abanumay, Sarah; Devanny, JosephThis dissertation investigates the relative advantages of generative artificial intelligence (GenAI) to cyber defensive and offensive operations. It examines how state and non-state actors can utilise GenAI, arguing that while GenAI can significantly enhance both offensive and defensive cyber operations, the extent of these benefits is determined by four interrelated factors: geostrategic priorities, economic resources, regulatory frameworks, and organisational capabilities. These factors collectively shape the cyber offensive-defensive balance, a central concept in this study for understanding GenAI's impact on cyber operations. The research follows a literature-based methodology guided by frameworks such as the NIST Cybersecurity Framework 2.0 and the Cyber Kill Chain. The dissertation is structured into three chapters: the evolution of GenAI in cybersecurity, an analysis of strategic debates and the offensive-defensive balance and an exploration of the factors shaping this balance. The findings provide valuable insights for maintaining cybersecurity in the GenAI era.17 0Item Restricted Integrating Artificial Intelligence Technologies in Sustainable Project Management(University of Exeter, 2024-07-04) Alqurashi, Abdullah; Roman, Jose MelenezSustainable project management encompassed the economic, environmental, and social aspects of a project to attain the project objectives in a sustainable manner. Nonetheless, the integration of AI technologies in sustainable project management was still low due to factors like inadequate knowledge of technical know-how, costs of implementing AI technologies, and resistance from the project team. This research aimed to identify the factors that hinder the application of AI in project management for sustainable practices and provided recommendations for enhanced application. The research sought to understand the status of AI adoption, challenges faced, and the impact of knowledge management practices on project performance based on the survey of 40 professionals in Saudi Arabia. The findings of this research enhanced the theoretical understanding of the topic by identifying that the level of awareness of AI is much higher than the level of its adoption. The research results show that although the level of awareness of AI technologies is relatively high, the implementation of the technologies is limited because of technical, financial, and organizational constraints. This research has also highlighted how knowledge management practice can be used to close this gap which can enhance increase in project performance, reduce costs and promote innovation. The research provided practical recommendations for organizations interested in using AI for sustainability and following best practices on a global level and in alignment with the vision of Saudi Arabia for the future. When applying these recommendations, professionals will be able to increase project efficiency, reduce costs, and promote innovation which contributes to sustainable development goals. This research presents a conceptual model that outlines how AI technologies can be applied in sustainable project management, fostering innovation and sustainable development. The research also highlights the necessity for future research to delve deeper into developing actionable frameworks and practical strategies for integrating AI into sustainable project management.66 0Item Restricted IS THE METAVERSEFAILING? ANALYSINGSENTIMENTS TOWARDSTHEMETAVERSE(The University of Manchester, 2024) Alharbi, Manal Dowaihi; Batista-navarro, RizaThis dissertation investigates Aspect-Based Sentiment Analysis (ABSA) within the context of the Metaverse to better understand opinions on this emerging digital environment, particularly from a news perspective. The Metaverse, a virtual space where users can engage in various experiences, has attracted both positive and negative opinions, making it crucial to explore these sentiments to gain insights into public perspectives. A novel dataset of news articles related to the Metaverse was created, and Target Aspect-Sentiment Detection (TASD) models were applied to analyze sentiments ex pressed toward various aspects of the Metaverse, such as device performance and user privacy. A key contribution of this research is the evaluation of the TASD architecture, TAS-BERT, and its enhanced version, Advanced TAS-BERT (ATAS-BERT), which performs each task separately, on two datasets: the newly created Metaverse dataset and the SemEval15 Restaurant dataset. They were tested with different Transformer based models, including BERT, DeBERTa, RoBERTa, and ALBERT, to assess performance, particularly in cases where the target is implicit. The findings demonstrate the ability of advanced Transformer models to handle complex tasks, even when the target is implicit. ALBERT performed well on the simpler Metaverse dataset, while DeBERTa and RoBERTa showed superior performance on both datasets. This dissertation also suggests several areas for improvement in future research, such as processing paragraphs instead of individual sentences, utilizing Meta AI models for dataset annotation to enhance accuracy, and designing architectures specifically for models like DeBERTa, RoBERTa, and ALBERT, rather than relying on architectures originally designed for BERT, to improve performance. Additionally, incorporating enriched context representations, such as Part-of-Speech tags, could further enhance model performance.9 0Item Restricted Leveraging Brain-Computer Interface Technology to Interpret Intentions and Enable Cognitive Human-Computer Interaction(Univeristy of Manchester, 2024) Alsaddique, Luay; Breitling, RainerIn this paper, I present the developed, integration, and evaluation of a Brain–Computer Interface (BCI) system which showcases the accessibility and usability of a BCI head- set to interact external devices and services. The paper initially provides a detailed survey of the history of BCI technology and gives a comprehensive overview of BCI paradigms and the underpinning biology of the brain, current BCI technologies, recent advances in the field, the BCI headset market, and prospective applications of the technology. The research focuses on leveraging BCI headsets within a BCI platform to interface with these external end-points through the Motor Imagery BCI paradigm. I present the design, implementation, and evaluation of a fully functioning, efficient, and versatile BCI system which can trigger real-world commands in devices and digital services. The BCI system demonstrates its versatility through use cases such as control- ling IoT devices, infrared (IR) based devices, and interacting with advanced language models. The system’s performance was quantified across various conditions, achiev- ing detection probabilities exceeding 95%, with latency as low as 1.4 seconds when hosted on a laptop and 2.1 seconds when hosted on a Raspberry Pi. The paper concludes with a detailed analysis of the limitations and potential im- provements of the newly developed system, and its implications for possible appli- cations. It also includes a comparative evaluation of latency, power efficiency, and usability, when hosting the BCI system on a laptop versus a Raspberry Pi.39 0Item Restricted Regulatory and Social Acceptance Challenges in Using Artificial Intelligence in Genomic Diagnostics in Saudi Arabia: Applying the Responsive Regulation and Innovation Diffusion Model.(University College London (UCL), 2024-08-28) Alderaa, Khalid; Jong, SimchaThis study explores the regulatory and social acceptance challenges of integrating Artificial Intelligence (AI) into genomic diagnostics in Saudi Arabia, using the Responsive Regulation and Innovation Diffusion model as theoretical frameworks. Methodology: The research employs a narrative review methodology, emphasizing regulatory frameworks, public trust, and the cultural perceptions that influence the adoption of AI technologies. Findings: The study identifies that, although AI holds significant promise for advancing genomic diagnostics, its full integration is hindered by regulatory gaps and a low level of social acceptance. The research emphasises the importance of creating a flexible and dynamic regulatory framework that can evolve with AI advancements. It also highlights the crucial role of stakeholder engagement and public education in building trust and ensuring that innovation progresses without compromising public safety. Limitations: Key limitations of the study include the restricted scope of the literature review, which primarily focuses on the European Union and Saudi Arabia, and the fast-paced development of AI technology, which may limit the long-term applicability of the proposed models. Practical Implications: To improve the adoption of AI in healthcare, this study recommends the implementation of regulatory sandboxes, which would allow AI innovations to be tested in controlled environments. Additionally, fostering public trust through transparency and education is critical to ensuring the successful integration of AI technologies in genomic diagnostics.23 0Item Restricted AI and the data shadow(Royal Central School for Speach and Drama, 2024-07) Duhaithem, Iyas; Jarvis, LiamMy aim with this research is to provide a setting to study AI as an active participant in a theatrical process. In real-time interactivity, co-creating narratives with human participants while creating an opportunity for the participants to explore their data shadow. The encounter and research seeks to expose the inner workings of AI tools, laying bare their processes for participants to observe and judge. This, in turn, allows the participants to foster a deeper understanding of how their personal data is being utilised and transformed by these technologies. To facilitate this study, my goal was to create a space that facilitated a 'data self-encounter', where participants interact with their personal data transformed through algorithmic processes using AI generative tools. By design, this setting consists of two sides that create the full image: the personal and digital sides. The personal concept aspect is fundamental in the project, making interactivity crucial, especially in finding a structure that serves to explore the participant's input, such as dreams or memories. As for the digital side, it was important to include elements that transform data that is specific to the individual. This came in many forms, such as voice and imagery. AI tools such as Deepfakes, Eleven labs and ChatGPT have made such manipulations widely accessible allowing for quick turnarounds in turn, enabling a new way to modify and repurpose personal data. In the end, this allows the participant to experience the ways they could explore their digital identities in an interactive theatrical setting. To lay the groundwork for this examination, it is essential to define key concepts that will underpin the final analysis.8 0Item Restricted A Systemic Literature Review Exploring the Impact of Artificial Intelligence on Marketing(University of Liverpool, 2024-09) Aldihnayn, Anfal; Peter, GuentherThe incorporation of artificial intelligence (AI) in marketing has transformed the field in recent years. In this regard, companies are now increasingly deploying AI solutions to improve consumer engagement and operational efficiency. This study investigates the power of AI in marketing, particularly through the lenses of the Technology Acceptance Model and Innovation Diffusion Theory on how this technology accelerates corporate productivity and profitability, enhances customer satisfaction, and brings hyper-personalization into marketing processes. The study employs a systematic literature review (SLR) approach, drawing on 37 peer-reviewed publications published between 2018 and 2024 to examine AI's impact across the marketing domain. Results reveal that AI significantly improves marketing strategies, helping companies to improve their financial performance, automate tasks, personalise their interactions with consumers, and enrich the overall customer experience. The findings of this study also supported the view that perceived usefulness (PU) and perceived ease of use (PEOU) are critical determinants for the incorporation of AI into marketing practices. Moreover, the relative advantage of AI as compared to other traditional marketing methods, especially in terms of automation and personalisation, has become one of the major drivers leading to its adoption. However, this study also emphasising the importance of addressing the ethical challenges including data privacy and algorithmic biases associated with using AI in marketing. This work offers researchers a direct and comprehensive overview of the extant knowledge role of AI in marketing. It concludes that the future of AI in marketing will continue to grow whereby companies worldwide will be able to tap into the power of AI for innovation and efficiency. Companies using AI will be better equipped to respond to the dynamic market demands and ensure a competitive advantage.43 0Item Restricted Regulating Maritime Autonomous Surface Ships (MASS): Challenges and Prospects.(Swansea University, 2024-09-28) Almulhem, Mubarak Ahmed; Tettenborn, Andrew; Amaxilati, ZoumpouliaMaritime Autonomous Surface Ships (or ‘MASS’) are a revolutionary technology that in the near- to medium-term are set to transform the maritime industry. As this technology reaches maturation, it is expected to lower shipping costs and improve safety, because it will greatly reduce – and maybe even entirely eliminate – the need for human seafarers and with them the potential for human error. That said, MASS also present significant regulatory challenges, because the law of the sea was developed with manned ships in mind and, particularly in respect fully autonomous ships with no human oversight, the existing regime will therefore struggle to accommodate MASS. Against this backdrop, this study assesses the challenges and prospects which MASS present for maritime regulation. The commentary looks particularly at the classification and design of MASS, the criteria for what makes a ship a ship in international law, and in particular the role of the master, along with how liability questions are impacted by AI systems. Overall, the commentary argues that, while the introduction of MASS present many not insignificant challenges to current maritime regulation, there is also a good prospect for regulation to play a proactive role in shaping the technology in a two-way process which can seek to maximise the benefits of MASS while minimising the potential for harm.21 0