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Due to the recent developments in the delivery of teaching-learning processes when COVID-19 hit the world with a health crisis and pandemic, it is crucial to look into the quality of courses delivered via online means or through distance education modality. This paper examines implementation and application of quality assurance (QA) landscape in Distance Education (DE). A documentary review using bibliographic inquiry is used as a methodology approach to gather relevant information to address the study questions. Previous studies on QA in DE are examined and arranged into themes using thematic analysis. Findings revealed that most of the literature on QA in DE in Africa and Asia based on the reviewed Open and Distance Learning (ODL) institutions are basically dealing with frameworks, outcomes and performance, instructional design, student services and challenges as well as parity in terms of quality with the traditional institutions. Therefore, this paper concludes that more studies are needed for QA in DE to match the post-COVID-19 trends on improving QA. This implies that there is a need to expand research on QA in DE to include areas of artificial intelligence. [ABSTRACT FROM AUTHOR]

A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural models for CDR are still in the theoretical phase, with low awareness and adoption of CDR in healthcare environments, susceptibility to several security threats, and the need to integrate federated learning in CDR systems. Overall, this paper would serve as a valuable reference for designing and implementing cutting-edge clinical data repositories in Africa and beyond. [ABSTRACT FROM AUTHOR]

Context and background Multidimensional Poverty (MP) considers poverty in multiple dimensions of deprivations such as health, education, energy, the standard of living and access to basic services. MP remains a major challenge in Africa, with a large proportion of the population living in MP. According to United Nations Development Programme (UNDP), Africa has shown the highest Multidimensional Poverty Index (MPI) having over 40% of its population living in MP. Goal and Objectives: This paper is a review, aimed at assessing the potential of the integration of satellite and Artificial Intelligence (AI) in mapping MP, with a specific focus on Africa. Methodology: Based on the reviews of past studies, the combination of satellite data such as nighttime light, daytime satellite imagery and high-resolution settlement data in combination with techniques such as field surveys, statistical correlation models (transfer learning) and AI (deep learning) has been applied in mapping MP. Results: The findings from studies show that the combination of satellite data and AI has the capability of providing more accurate and granular MP maps, compared to the traditional approach. Again, this paper explains the concept of MP with a specific focus on Africa and presents a map depicting the current MPI in African countries. Finally, pitfalls especially in the accuracy, granularity and frequency of MP data were identified. Consequently, the satellite and AI approaches are recommended for more accurate, frequent, cost-effective and granular data, required in mapping poverty and design of interventions that effectively address the needs of the vulnerable populations in Africa. [ABSTRACT FROM AUTHOR]

Context and background The incorporation of artificial intelligence (AI) into businesses has become increasingly widespread in recent years, particularly in developed countries, fundamentally reshaping the landscape of entrepreneurship and innovation. AI presents numerous opportunities for small businesses, including those involved in cross-border trade. It has the potential to transform various aspects of business operations, such as customer interactions, marketing research, understanding consumer behavior, optimizing routes, and forecasting sales and marketing trends. Additionally, AI can contribute to the formalization of business processes, and assist these businesses in adapting more effectively to the ever-changing dynamics of the market. However, the successful implementation of AI necessitates a meticulous assessment of AI applications to ensure alignment with the objectives and resources of each individual small business. Goal and Objectives: The primary focus of this paper is to explore the potential of Artificial Intelligence in enhancing Africa's cross-border trade. The paper will address the following research questions: What are the effective applications of artificial intelligence in cross-border informal trade in Africa, and what are the limitations? What does the future hold for African countries in terms of utilizing artificial intelligence for cross-border trade? How can African informal cross-border traders and their stakeholders benefit from incorporating artificial intelligence in their activities? Methodology: The research is purely empirical research based on observation and measurement of phenomena, as directly experienced by the researcher. Results: AI can be effectively applied by Informal Cross-Border Traders (ICBT) to enhance their businesses and enhance competitiveness. There are several AI applications accessible to ICBT within their operational context. Although the adoption and utilization of AI in Africa are still in their infancy, there is considerable promise for the future. Africans must address the challenges hindering the adoption and utilization of AI, as technology is advancing rapidly, and opportunities await those who embrace it. [ABSTRACT FROM AUTHOR]

African public administrations are in an especially difficult position with respect to the adoption of artificial intelligence (AI), in both how they harness its benefits but also in managing potential harms. Through a narrative review of existing research, this paper synthesises findings from previous digital government implementations on the African continent, and considers the implications for AI use. The analysis is centred on the way public officials are bypassed or empowered to work with emerging technologies, and the influence of public service values on their adoption. Four concerns emerge as relevant: Integrity of recommendations provided by decision-support systems, including how they are influenced by local organisational practices and the reliability of underlying infrastructures; Inclusive decision-making that balances the (assumed) objectivity of data-driven algorithms and the influence of different stakeholder groups; Exception and accountability in how digital and AI platforms are funded, developed, implemented and used; and an expectation of Complete understanding of people and events through the integration of traditionally dispersed data sources and systems, and how policy actors seek to mitigate the risks associated with this aspiration. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to investigate the level of adoption of artificial intelligence(AI) to support library services delivery in university libraries in Africa. Qualitative research method was adopted to collect data. A preliminary survey of 102 university libraries in English speaking countries in Africa was conducted to identify the university libraries that have adopted AI in their libraries. Content analysis was used to analyse the responses. The study found that the only few university libraries in Africa have adopted AI technologies such as Chatbot, ChatGPT, LibKey from ThirdIron, robots, RFID technology and Grammarly. These AI technologies are used to render different library services like answering of directional and ready reference questions posed by library users, serving as a knowledge base for cataloguing information of library materials, self-check-out machine for books, used as a marketing tool for the library, tool for statistics evaluation and recommendations, assisting in the charging and discharging of library materials, etc. Lack of funds to acquire the AI tools, training of librarians, and lack of full knowledge of AI were the most mentioned challenges associated with adoption of AI in the libraries. The study recommended a formulation of policy to guide the adoption of new technologies such as AI, and training and re-training of librarians through workshops equip librarians with skills needed to effectively use the AI technologies. [ABSTRACT FROM AUTHOR]

The role of libraries in preparing students to thrive during their studies and innovate after university is growing in importance. Information is more easily accessible through digital channels and is increasingly abundant. Generative Artificial intelligence (AI) adds to this reality and increases the need for digitally driven information literacy skills. This paper aims to guide librarians by discussing the digitalization of information creation, retrieval, and use. It recommends the training of both digital and information literacy for students. Librarians are called upon to provide clear guidelines to their universities to steer the use of generative AI. The implications of digital information sources and generative AI are discussed with the role of librarians in context. Information literacy and digital literacy are related. Academic libraries should include digital topics in information literacy training. Information literacy should be trained at the start of university education and before students begin dissertation writing. University libraries should propose guidelines for the use of generative artificial intelligence tools by students. [ABSTRACT FROM AUTHOR]

This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term datasets for surface water research. Various sensors with differing spatial resolutions are discussed, with high-resolution multispectral sensors providing superior spatial resolution but at higher costs. Conversely, dualsensor approaches, incuding optical sensors (Sentinel-2 and Landsat), radar satellites (Sentinel-1 and RADARSAT) and UAVs were investigated. The review further examines the efficiency and applicability of traditional algorithms such as the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), and automated water extraction index (AWEI) in detecting and delineating surface water resources. Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities. [ABSTRACT FROM AUTHOR]

China is the largest foreign information and communications technology (ICT) investor in Africa, and Chinese telecommunication companies including Huawei, CloudWalk, and Transsion have brought a range of artificial intelligence (AI) technology to Africa, from face recognition to smart cities. Existing analysis of China's AI impact in Africa, however, habitually falls into a Cold War-style narrative of 'authoritarianism' against the backdrop of 'liberal democracy.' This article calls for going beyond the oversimplified paradigm and proposes to examine the new phenomenon through a more sophisticated and critical lens. First, I show how (English language) media's investigations and academia's analysis of China's AI policy and its global impact (particularly in Africa) usually concentrate on a deep concern about the exportation of authoritarianism from Beijing. The article then demonstrates that the 'authoritarianism vs liberal democracy' model is problematic, if not outdated, and particularly insufficient to capture the multi-layered and overlapping realities of authoritarian and democratic moves by states around the world and the distinguishing properties of AI technologies. Based on a critical appraisal of the notions of surveillance capitalism and data colonialism and the recent empirical evidence from Africa in the third and fourth sections, I propose that these two concepts are more productive analytical frameworks for understanding what is going on in Africa related to the presence of China's AI, as well as for updating and moving forward the debates about whether China is colonizing Africa. [ABSTRACT FROM AUTHOR]

This study investigates the impact of ChatGPT on Chinese L2 writing proficiency among African students, aligning with the Sustainable Development Goals (SDGs) for quality education and reduced inequalities. Employing a mixed-methods approach, the research involved 46 participants to quantitatively assess improvements in syntactic and lexical complexity, accuracy, and fluency. Qualitative insights were garnered through semi-structured interviews, revealing positive perceptions of ChatGPT in enhancing writing skills, yet highlighting challenges like technological access and economic constraints. The findings suggest that while ChatGPT effectively enhances Chinese L2 proficiency, its integration in African educational contexts requires addressing infrastructural and pedagogical barriers. This study contributes to the discourse on Artificial Intelligence in language education and its role in advancing African sustainable educational practices. [ABSTRACT FROM AUTHOR]

Malaria is a life-threatening disease caused by Plasmodium parasites that enter the bloodstream via insect vectors, such as mosquitos. The disease has devastated parts of Africa and the wider human population throughout human periods and thus remains a significant global health challenge due to the difficulty in the procurement of a vaccine and the nature of its spread. While many have turned towards preventative methods, the first thing that must happen is a timely and accurate diagnosis of malaria. This is crucial for the effective management and control of the disease. However, the current diagnostic methods have various limitations, including cost, availability, and accuracy. This review aims to unmask the challenges of malaria diagnosis by exploring existing diagnostic techniques and shedding light on emerging technologies that show promise in improving malaria detection. Furthermore, it examines the potential of innovative approaches, such as artificial intelligence (AI) and molecular diagnostics, in transforming malaria diagnosis and contributing to global efforts toward malaria elimination. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) tools are now used in our daily lives. This study aimed to explore the level of awareness, perceived benefits, threats, attitudes, and level of satisfaction with AI tools among individuals within higher education in Asia and Africa. A cross-sectional study was conducted in August 2023. Snowball sampling was used with a convenience sample of 815 highly educated Asian and African participants from 11 countries. About 56% of participants have Bachelor's degrees. 312 participants (38%) were unaware of AI tools and AI tools were used rarely by 316 (63%) of 503 participants who were aware of them. ChatGPT is the most popular of this study's AI tools (N=405, 81%). Participants who used AI tools reported greater benefits than those who did not (p < 0.05). Of the four educational groups, those with a Master's degree reported a higher AI tool threat than those with a Diploma (P < 0.05). Female participants reported more AI-related threats than males (P < 0.05). In conclusion, this research is important because of the rapid development of modern technology around the world. Nevertheless, Asia and Africa still lag behind developed nations in AI technology awareness. [ABSTRACT FROM AUTHOR]