School of Computing and Innovative Technologies

Perceptions Of Generative AI in the Global South: A Scoping Review

New generative AI (GenAI) tools are largely a product of the Global North but have rapidly spread worldwide. This scoping review examines the current perceptions of Generative AI in higher education across the Global South by analysing 75 papers published between 2022-2025. Following the PRISMA-ScR methodology, the review categorised the findings into five main areas: GenAI acceptance and adoption, implications and challenges, academic integrity considerations, educational practices, and equity concerns. The analysis reveals that GenAI offers transformative possibilities for personalised learning, research support, and administrative efficiency in higher education across the Global South, but its implementation faces significant barriers, including infrastructure limitations, human capital deficiencies, ethical concerns, inadequate policy frameworks, and contextual challenges. Notably, equity considerations have received the least research attention despite their critical importance in inclusive education. This review also identified substantial gaps in the literature, including limited geographic representation, stakeholder imbalance, and insufficient exploration of long-term outcomes. The authors recommend that future research prioritise equity-centred approaches, methodological diversity, contextual specificity, implementation science, and interdisciplinary collaboration to better understand how Generative AI can enhance educational opportunities without reinforcing existing disparities across the diverse contexts of the Global South.

School of Business

Reimagining the Artificial Intelligence Assessment Scale: A refined framework for educational assessment

Higher education institutions, educators, and students continue to grapple with the wide-ranging implications of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI). The ability of advanced GenAI models to complete educational assessments continues to be one of the most pressing issues for academia. In early 2024, we published the AI Assessment Scale (AIAS), which describes a practical framework for addressing GenAI use in educational assessment. The AIAS was well received and has been implemented in hundreds of institutions worldwide and translated into 30 languages. Building on our experience using the AIAS and drawing on feedback and critiques received worldwide, in this paper, we provide an updated version of the AIAS. The updated AIAS encompasses several important developments, including clarifying the theoretical basis of the scale through social constructivist principles, developing the AIAS to function as an assessment redesign framework to enhance validity, and adjusting the visual assets of the AIAS to function inclusively and non-hierarchically. Furthermore, in anticipation of future developments in the field of AI and the increasing focus on preparing students to function in an AI-augmented world, we introduce an additional level, ‘AI exploration’. Through this paper we also seek to clarify both the intended and unintended applications of the AIAS. We provide implementation guidance through practical examples and explain how the AIAS functions as both a communication tool and a framework for task redesign that strengthens validity.

School of Computing and Innovative Technologies

User behaviour’s contribution to better Cyber Security Management

According to theoretical and empirical knowledge, cybersecurity awareness is a crucial issue in cyber security. The main actors in cyber security are people, and one way to reduce risk in cyberspace is to increase knowledge of security concerns. Companies lose money as a result of data breaches and production losses brought on by cyberattacks. Consequently, there has been a surge in research endeavours aimed at comprehending the cybersecurity behaviours of users. The benefit of knowing user behaviours is that researchers and security professionals may utilize this information to start altering behaviours for the sake of cybersecurity. Similar cybersecurity behaviours have been categorized by several research, while the naming systems used vary. Sanctions, a decline in customer loyalty, and damage to one's brand may all arise from data breaches. Business continuity is also impacted by cyberattacks, which make it difficult for organizations to maintain constant production. This paper aims to demonstrate that, in addition to computer science research, behavioural sciences that study user behaviours can offer useful strategies to improve cyber security and lessen the impact of attackers' social engineering and cognitive hacking tactics (i.e., disseminating misleading information). Thus, in this study, we provide fresh insights on the psychological characteristics and individual variances of computer system users that account for their susceptibility to cyberattacks and crimes. Our investigation shows that different computer system users have different cognitive capabilities, which affects their ability to defend against information security threats. In order to improve network and information security, we identify research gaps and suggest possible psychological techniques to help computer system users follow security requirements.

School of Computing and Innovative Technologies

Eco-conscious multi-factor authentication protocols for energy-aware wireless networks

The rapid advancement of wireless technologies has enabled the widespread deployment of wireless sensor networks (WSNs) in various applications, including environmental monitoring, military surveillance, and smart infrastructure. However, due to their decentralized design, limited energy resources, and open wireless communication channels, WSNs are extremely vulnerable to attacks such as eavesdropping, impersonation, and tampering. Existing security solutions frequently ignore the fundamental energy limits of sensor nodes, resulting in rapid battery depletion and shortened network longevity. To solve this issue, the goal of this research is to provide an Energy-Efficient Elliptic Diffie Clustering Technique (3EDCT) method that is suited for energy-conscious WSNs that balance security robustness with low energy usage. The proposed technique comprises two major components: Elliptic Curve Diffie–Hellman (ECDH) and Energy-Efficient Hybrid Clustering Technique (EEHCT). Nodes are grouped into optimum clusters using EEHCT, with cluster heads chosen depending on residual energy and proximity. EEHCT rotates cluster heads dynamically to avoid early energy exhaustion. During authentication, ECDH is utilized to exchange public keys securely and efficiently between nodes and cluster chiefs. Multi-factor authentication is used to verify the identification of nodes. The model is evaluated based on parameters such as energy usage, node longevity, and authentication latency. The results reveal that the 3EDCT method reduces overall energy usage, increases network lifetime (36,865 s), and achieves a significant reduction in latency (0.114 s), as well as a reduction in communication and computation overhead (1472 bits and 4.92 ms) compared to baseline models. The 3EDCT method, combined with ECDH and EEHCT, offers a scalable, secure, and energy-aware solution for next-generation WSN applications.

School of Hospitality and Tourism

Can synthetic avatars replace lecturers? An exploratory international study of higher education stakeholder perceptions

Advances in technologies which use Generative Artificial Intelligence (GenAI) to mimic a person’s likeness or voice have led to growing interest in their use in educational contexts. However, little is known about how key stakeholders (teaching faculty and professional staff) perceive and intend to use these tools. This study investigates higher education employees’ perceptions and intentions regarding the use of synthetic avatars (alternatively known as deepfakes) through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Using a mixed-methods approach that combined quantitative survey data (n = 173) with qualitative text response, we found that academic stakeholders demonstrated a relatively low intention to adopt these technologies (M = 41.55, SD = 34.14) and held complex, often contradictory views about their implementation. Stakeholders identified potential benefits, including enhanced student engagement through interactions with historical figures, improved accessibility through voice synthesis, and reduced workload in content creation. However, they expressed significant concerns about the exploitation of academic labour, institutional cost-cutting leading to automation, degradation of human relationships in education, and broader societal impacts, such as environmental costs and information validity. Quantitative analysis revealed that adoption intentions were most strongly associated with hedonic motivation, with a gender-specific interaction in the evaluation of price value. Qualitative findings highlighted significant concerns regarding ethical implications, resource inequities, and the impact on professional identity. These results suggest that traditional technology acceptance models should be expanded to consider broader ethical and structural factors. Based on these findings, we propose a three-pillar framework for implementing synthetic avatar technologies in higher education that emphasises establishing robust institutional policies and governance structures, developing comprehensive professional development and support systems, and ensuring equitable resource allocation guided by evidence-based implementation strategies. This study enhances our understanding of how emerging AI technologies can be thoughtfully integrated into higher education while maintaining academic integrity and professional autonomy of educators.

School of Business

Can synthetic avatars replace lecturers? An exploratory international study of higher education stakeholder perceptions

Advances in technologies which use Generative Artificial Intelligence (GenAI) to mimic a person’s likeness or voice have led to growing interest in their use in educational contexts. However, little is known about how key stakeholders (teaching faculty and professional staff) perceive and intend to use these tools. This study investigates higher education employees’ perceptions and intentions regarding the use of synthetic avatars (alternatively known as deepfakes) through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Using a mixed-methods approach that combined quantitative survey data (n = 173) with qualitative text response, we found that academic stakeholders demonstrated a relatively low intention to adopt these technologies (M = 41.55, SD = 34.14) and held complex, often contradictory views about their implementation. Stakeholders identified potential benefits, including enhanced student engagement through interactions with historical figures, improved accessibility through voice synthesis, and reduced workload in content creation. However, they expressed significant concerns about the exploitation of academic labour, institutional cost-cutting leading to automation, degradation of human relationships in education, and broader societal impacts, such as environmental costs and information validity. Quantitative analysis revealed that adoption intentions were most strongly associated with hedonic motivation, with a gender-specific interaction in the evaluation of price value. Qualitative findings highlighted significant concerns regarding ethical implications, resource inequities, and the impact on professional identity. These results suggest that traditional technology acceptance models should be expanded to consider broader ethical and structural factors. Based on these findings, we propose a three-pillar framework for implementing synthetic avatar technologies in higher education that emphasises establishing robust institutional policies and governance structures, developing comprehensive professional development and support systems, and ensuring equitable resource allocation guided by evidence-based implementation strategies. This study enhances our understanding of how emerging AI technologies can be thoughtfully integrated into higher education while maintaining academic integrity and professional autonomy of educators.

School of Business

Decoding Employee Turnover: How Job Satisfaction, Commitment, Work-Life Balance, and Compensation Shape Intentions in Malaysia’s Service Industry

The demanding nature of the service sector in Malaysia, which involves long hours and pressure at work, causing a significant percentage of employees either leave or change occupations. It creates disruptions due to the constant hiring and training of new staff, which lowers business profitability. Thisstudy investigates the role of job satisfaction, organisational commitment, work-life balance, compensation and benefits on employee turnover in the services industry of Malaysia. Data were collected from 400 respondents who were fulltime employees from high turnover rate industries such as finance, tourism, food and beverage, telecommunication and ICT firms. The study used Social Exchange Theory (SET) as an orientation because it considers how workers weigh their contributions against the experiences that they have had with their organisations underlining that motivation or commitment underpins low levels of employee turnover. The data were analysed usingmultiple regression analysis in order to investigate the association between the independent variables and employee turnover. This study exhibits how retention strategies can be improved with respect to job satisfaction, organisational commitment, work-life balance, andcompensation. The findings of this study iscontributing to the understanding of the strategies that can be effectively applied in managing the retention of human resources, thereby enhancing organisational efficiency and reducing turnover in the service sector.

School of Computing and Innovative Technologies

AI-Enhanced Pilot-Assisted Angle-of-Arrival Estimation for Wearable Devices in Rician Fading Channels: A Low-SNR Focused Method

Accurate angle-of-arrival (AoA) estimation is critical for precise localisation in wearable devices, particularly in challenging wireless environments such as Rician fading with low signal-to-noise ratios (SNRs). This paper proposes a pilot-assisted AoA estimation technique that integrates pseudo-random permutations and Walsh sequences within an OFDM-based transmission framework. The method preserves phase coherence and enhances spatial resolution by optimising pilot allocation and leveraging advanced signal processing. Comprehensive MATLAB simulations show high robustness: At −38dB (per-subcarrier, per-snapshot SNR), the ≈1.5∘ RMS is achieved by aggregating across L snapshots and multiple subcarriers (see Table 12 for K-factor scenarios), with sub-degree accuracy at moderate-to-high SNRs. Furthermore, a lightweight, one-dimensional (1D) convolutional neural network (CNN) reduces residual carrier-frequency offsets by over 30%, highlighting a promising synergy between classical signal processing and data-driven learning. Comparative analysis against state-of-the-art techniques and a discussion of computational complexity are provided, underscoring the suitability of the proposed method for next-generation wearable and IoT direction-finding applications.

School of Computing and Innovative Technologies

Edge Artificial Intelligence: Foundations, Techniques, and Applications

Secure your expertise in the next wave of computing with this essential book, which provides a comprehensive guide to Edge AI, detailing its foundational concepts, deployment strategies, and real-world applications for revolutionizing performance and privacy across various industries. Edge AI has the potential to bring the computational power of AI algorithms closer to where data is generated, processed, and utilized. Traditionally, AI models are deployed in centralized cloud environments, leading to latency issues, bandwidth constraints, and privacy concerns. Edge AI addresses these limitations by enabling AI inference and decision-making directly on edge devices, such as smartphones, IoT sensors, and edge servers. Despite its challenges, edge AI presents numerous opportunities across various domains. From real-time health monitoring and predictive maintenance in industrial IoT to personalized recommendations in retail and immersive experiences in augmented reality, edge AI has the potential to revolutionize how we interact with technology. This book aims to provide a comprehensive exploration of edge AI, covering its foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, emerging trends, and real-world applications. This guide is essential for anyone pushing the boundaries to leverage edge computing for enhanced performance and efficiency.

School of Computing and Innovative Technologies

Optimizing Evacuations Using Agent-Based Modeling