Search

Recent advancements in generative artificial intelligence (GAI) have sparked debates and research on its transformative potential in education. This study explored how a specific group of educators partner with GAI tools, particularly ChatGPT, to complement and enhance their teaching. Within an interpretative paradigm, the study used an exploratory case study design to investigate how 13 educators enrolled as students in a structured master's programme at an open distance learning university in South Africa use GAI tools in their teaching. Their posts on a discussion forum were used to collect data. Findings reveal that they actively employ GAI to streamline their teaching practices, specifically to personalized tutors, simplifying English texts and translations, assessments, lesson planning, and critical thinking tasks. The study also highlights the need to understand the limitations and boundaries of GAI, including concerns about accuracy, biases and reliability. Importantly, the absence of institutional guidelines on GAI raises questions about the necessity for clear guidelines and policies to ensure responsible and ethical integration in educational contexts. The paper concludes by emphasizing the transformative potential of GAI and the fact that it will increasingly act as a partner in teaching and influence modern teaching practices. Ultimately, GAI's foothold in education is undeniable, challenging educators and policymakers to navigate its implications while maximizing its potential to support effective teaching.

Lecture Notes in Computer Science (LNCS) is a globally recognised publication outlet for the field of Computer Science, including in South Africa. In this study, spanning from 1973 to 2022, we investigated the research participation of South African based authors in LNCS. The publication output and citation impact of these authors were compared to the global Computer Science and LNCS output. The authorship patterns and collaborative behaviour of South African LNCS papers were explored, and a keyword or topic analysis also conducted. Of the total of 518 662 LNCS papers published globally between 1973 and 2022, South African based researchers contributed 1150 papers (0.22%). The LNCS papers from South Africa exhibit a strong collaborative publication culture, with 1043 (91%) co-authored and 107 (9%) singleauthored works. Local LNCS researchers prefer institutional collaboration (43%), followed by international (37%) and national collaboration (11%). Europe emerged as the most significant collaboration partner for LNCS researchers in South Africa. Of the 1150 papers, 836 (73%) had received citations, while 314 (27%) had not. On average, papers published by South African based authors received 6.05 citations, compared to the global LNCS average of 9.49 citations per paper. A keyword analysis revealed that the majority of papers by South African authors focus on artificial intelligence. The results indicate that, although LNCS serves as a reputable dissemination platform for Computer Science research output both globally and locally, South African authors should consider publishing more journal articles to build and improve their researcher profiles. Significance: * The study shows that LNCS is the most frequent publication outlet for Computer Science researchers, globally and in South Africa. * The study offers insight into the publication output, authorship patterns, collaborative behaviour and citation impact of South African based Computer Science researchers. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic and the shift to digital learning materials have dramatically reduced the use of paper-based textbooks in higher education. In the field of engineering, students need a comprehensive understanding of mathematical concepts, which can be achieved through the use of e-textbooks. These digital learning materials can provide a more comprehensive and effective learning experience by incorporating a variety of multimedia elements, such as audio, visual aids, and interactive features. Rationale/Relevance: Mathematics students are expected to possess critical problem-solving skills, yet they are rarely asked to elaborate on and explain their mathematical reasoning and concepts through written argument in the engineering mathematics classroom. While students may excel in formula manipulation, they may still possess misconceptions about mathematical principles and concepts. A reliance on mechanical and procedural approaches, such as formula application, without proper conceptual understanding can result in serious misunderstandings of mathematics and its real-world applications. Without the ability to reason about mathematical concepts, students may struggle to connect what they are learning in class with real-world scenarios, leading to difficulties in solving practical problems. It is, therefore, crucial to foster conceptual understanding and critical reasoning skills in mathematics education, rather than solely relying on memorisation, in order to equip students with the necessary skills to succeed in their careers. Research Problem/ Research Question: This study will answer the following research question:How effective are e-textbooks in promoting conceptual understanding of engineering mathematics? Aim of the Study/Focus: The purpose of this study is to investigate conceptual learning in engineering mathematics using an e-textbook as a learning tool. Methodology: This qualitative case study explores e-textbooks as adaptive technology, with functionalities that include artificial intelligence allowing students to develop their understanding by interacting with digital text, watching videos of real-world mathematics concepts, and responding to quick quizzes on concepts; as well as practising and mastering further mathematical principles and concepts. It focuses on first-year engineering students and lecturers at a University of Technology in South Africa. The analysis of interview recordings was done with ATLAS.ti. analytical software. Theoretical Framework: The data from this study was analyzed through the lenses of cultural historical activity theory (CHAT) and Vosniadou's notion of conceptual change, allowing the researchers to explain complex real-world situations that students experience when engaging with the e-textbook to solve mathematics problems. Recommendations: The work presented here has implications for future studies of conceptual learning in mathematics research and may provide opportunities around learning engineering mathematical concepts, particularly in the context of developing countries. This is because it offers novel instructional approaches that are tailored to the specific needs and challenges of these countries.

The current article used real data to demonstrate the analysis and synthesis of Mixed Methods Research (MMR) data with generative Artificial Intelligence (Gen AI). I explore how reliable and valid Gen AI data outputs are and how to improve their use. The current content is geared towards enhancing methodological application regardless of field or discipline and includes access to a prompt library and examples of using outputs. The demonstration data used emanated from a study done in South Africa, with a quantitative sample size of 969 first-year engineering students and, for the qualitative part, 14 first-year students. In the current article, I compare my original analysis to ChatGPT results. Generative AI as a mind tool is best used with human insight, and I found this to be especially true when coding qualitative data. ChatGPT produced generic codes if asked to do inductive coding, and the results improved when training the Gen AI on human examples, which led to moderate and significant correlations between human and machine coding. The quantitative analysis was accurate for the descriptive statistics, but the researcher had to use best judgment to select the correct inferential analysis. Quantitative and qualitative analysis should be conducted separately in generative AI before asking the Chatbot for help with mixed methods results. In the current paper, I give guidelines and a tutorial on how to use chatbots in an ethically responsible and scientifically sound manner for research in social and human sciences.

Background: The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022., Methods: The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was "Ethics of AI in Global Health Research". The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022., Results: We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships., Conclusions: The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research., (© 2024. The Author(s).)

Artificial intelligence (AI) is an important enabler of the fourth and fifth industrial revolutions. It has the potential to empower intelligent production systems that are central to industry 4.0 and help place worker wellbeing and the planet at the centre of industry 5.0 processes. However, few studies have considered the different roles of AI in industry 4.0 and 5.0. This paper reports on a bibliometric review of 2887 documents examining the use of AI for industry 4.0 and industry 5.0. We found that scholarly activity increased rapidly from 2018 onwards and can be attributed to a large variety of countries, including India, South Africa and China. Our analysis reveals four clusters in the literature, with two showing particular promise for researchers notably around the themes of industry 5.0 and deep learning. Further insights are presented and discussed, followed by a future research agenda. [ABSTRACT FROM AUTHOR]

The current disconnect in policy development and management of the water, energy, and food resources threatens their security of supply despite their inextricable interlinkage, commonly known as Water-Energy-Food nexus. The security of these basic human needs is aggravated by climate change's cross-cutting role, which impacts their availability. These apprehensions suggest that contemporary methods are required to improve and enhance the management of the food, water, and energy resources within the climate change discourse. This study applies Systems Thinking methodologies to foster collaboration amongst key stakeholders within Eskom, the electricity generating sector in South Africa. This was done through several simulation workshops held amongst employees within the generation, transmission, distribution, and corporate employees. The workshops demonstrated the ability of the systems thinking approach to enable stakeholders to apply the “nexus thinking” in managing the WEF sectors within the climate change discourse. The study concludes by recommending the application of this simulation within policy development and other key sectors to enable a broader application of nexus thinking, thus assisting in addressing the silo approach within these resource management. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are powerful data-oriented "black-box" algorithms capable of assessing and delineating linear and multifaceted non-linear correlations between the dependent and explanatory variables. Through the years, neural networks have proven to be effective and robust analytical techniques for establishing artificial intelligence-based tools for modelling, estimating, and projecting spatial and temporal variations in water bodies. Accordingly, ANN-based algorithms gained increased attention and have emerged as practical alternatives to traditional approaches for hydro-chemical analysis. ANNs are among the widely used computer systems for modelling surface water quality. Considering their wide recognition, resilience, flexibility, and accuracy, the current study employs a neural network-based methodology to construct a novel water quality index (WQI) model suitable for analysing South African rivers. The feed-forward, back-propagated multilayered perceptron model has three parallel-distributed neuron layers interconnected with seventy weighted links orientated laterally from left to right. First, the input layer includes thirteen neuro-nodes symbolising thirteen explanatory variables, including NH3, Ca, Cl, Chl-a, EC, F, CaCO3, Mg, Mn, NO3, pH, SO4, and turbidity (NTU). Second, the hidden layer consists of eleven neuro-nodes accountable for computational tasks. Lastly, the output layer features one neuron responsible for conveying network outcomes using a single-digit WQI rating extending from zero to one hundred, where zero represents substandard water quality and one hundred denotes exceptional water quality. The AI-based model was developed using water quality data obtained from six monitoring locations within four drainage basins under the management of the Umgeni Water Board in the KwaZulu-Natal Province of South Africa. The dataset comprises 416 samples randomly divided into training, testing, and validation sets using a proportional split of 70:15:15%. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) technique was utilised to conduct backpropagation training and adjust synapse weights. The dependent variables are the WQI scores from the universal water quality index (UWQI) model developed specifically for South African river basins. The ANN demonstrated enhanced efficiency through an overall correlation coefficient (R) of 0.985. Furthermore, the neural network attained R-values of 0.987, 0.992, and 0.977 for the training, testing, and validation intervals. The ANN model achieved a Nash–Sutcliffe efficiency (NSE) value of 0.974 and coefficient of determination (R2) of 0.970. Sensitivity analysis provided additional validation of the preparedness and computational competence of the ANN model. The typical target-to-output error tolerance for the ANN model is 0.242, demonstrating an adequate predictive ability to deliver results comparable with the target UWQI, having the lowest and highest index ratings of 75.995 and 94.420, respectively. Accordingly, the three-layer neural network is scientifically sound, with index values and water quality evaluations corresponding to the UWQI results. The current research project seeks to document the processes used and the outcomes obtained. [ABSTRACT FROM AUTHOR]

AI-based simulations for educational and assessment purposes are gaining global recognition. Informed by cultural comparison research, this study investigates cross-country variations in users' utilization and perceptions of a simulation-based assessment. Specifically, we conducted a comparative analysis between a sample of South African and Canadian students to uncover potential differences in assessment scores, communication patterns, and reactions vis-a-vis a simulation assessment for evaluating teamwork skills. Data were collected from over 500 undergraduate students in South Africa and Canada who completed a simulation assessment and reported their reactions and perceptions. The findings yielded several noteworthy observations. First, South African students attained higher assessment scores than Canadian students; although, the difference did not quite reach statistical significance at p < 0.05. Second, significant variations were observed in the quantity and style of communication. South African students used fewer words and more polite language, while Canadian students tended to use more decisive language and provided more explanations and help to their virtual teammates. Third, South African students were more likely to perceive their virtual teammates as "human" and were less concerned whether they were real people or virtual. Lastly, compared with their Canadian counterparts, South African students reported more positive reactions and perceived the assessment to be more accurate. These findings warrant further investigation. [ABSTRACT FROM AUTHOR]