Purpose: The continuously evolving Artificial Intelligence (AI) oriented artefacts are transforming every facet of daily life, including the education sector. Recognising education’s critical role in enhancing citizens’ quality of life, it becomes essential to explore how AI can revolutionise education, including learning and teaching processes, educational settings, learning outcomes, among other components. However, the complexity and scale of AI developments pose significant challenges to its effective implementation in educational contexts. Method: Integrating AI in education has substantial implications on learning and teaching. On the one hand, AI might increase the accessibility to personalised and satisfactory learning experiences. On the other hand, challenges such as bias, privacy and digital divide, exacerbate the difficulties associated with deploying AI effectively in educational settings. A detailed qualitative analysis of principal AI frameworks, models and theoretical foundations has been undertaken to identify and assess factors influencing these challenges. Findings: This study presents an holistic examination of AI in education, focusing on theoretical frameworks, models and practical implications. It derives a conceptual framework aimed at promoting high-quality education, thereby supporting the achievement of Sustainable Development Goal (SDG) 4, which emphasises Quality Education. Originality: This conceptual framework uniquely addresses the gap between the efficient use of AI in education and the attainment of SDG 4. Furthermore, it lays the groundwork for combining research efforts to address the ever-expanding research gap concerning effective employment of AI in education and broader life contexts. The study also suggests several directions for developing contextualised strategies for the implementation of AI in education. Research Limitaions: This study aims at gaining meaningful insights from the application of AI in education; however, it cannot accommodate every single educational or learning approach. Therefore, testing this framework with real world contextualised scenarios remains essential to avoid abstract outcomes. Keywords: Artificial Intelligence; Education; Quality Education; Sustainable Development Goals; Technology Enhanced Learning; SDG 4. Citation: Hammad, R. (2025): A Framework for Effective Utilisation of Artificial Intelligence in Education. World Journal of Science, Technology and Sustainable Development (WJSTSD), Vol. 20, Nos 1/2, pp. WASD: London, United Kingdom.

PURPOSE: The continuously evolving Artificial Intelligence (AI) oriented artefacts are transforming every facet of daily life, including the education sector. Recognising education’s critical role in enhancing citizens’ quality of life, it becomes essential to explore how AI can revolutionise education, including learning and teaching processes, educational settings, learning outcomes, among other components. However, the complexity and scale of AI developments pose significant challenges to its effective implementation in educational contexts. METHOD: Integrating AI in education has substantial implications on learning and teaching. On the one hand, AI might increase the accessibility to personalised and satisfactory learning experiences. On the other hand, challenges such as bias, privacy and digital divide, exacerbate the difficulties associated with deploying AI effectively in educational settings. A detailed qualitative analysis of principal AI frameworks, models and theoretical foundations has been undertaken to identify and assess factors influencing these challenges. FINDINGS: This study presents an holistic examination of AI in education, focusing on theoretical frameworks, models and practical implications. It derives a conceptual framework aimed at promoting high-quality education, thereby supporting the achievement of Sustainable Development Goal (SDG) 4, which emphasises Quality Education. ORIGINALITY: This conceptual framework uniquely addresses the gap between the efficient use of AI in education and the attainment of SDG 4. Furthermore, it lays the groundwork for combining research efforts to address the ever-expanding research gap concerning effective employment of AI in education and broader life contexts. The study also suggests several directions for developing contextualised strategies for the implementation of AI in education. RESEARCH LIMITATIONS: This study aims at gaining meaningful insights from the application of AI in education; however, it cannot accommodate every single educational or learning approach. Therefore, testing this framework with real world contextualised scenarios remains essential to avoid abstract outcomes. KEYWORDS: Artificial Intelligence; Education; Quality Education; Sustainable Development Goals; Technology Enhanced Learning; SDG 4. CITATION: Hammad, R. (2024): Artificial Intelligence Enabled Framework for Quality Education: A Mechanism to Leverage SDG Applications. In Ahmed, A. (Ed.): World Sustainable Development Outlook 2024, Vol. 20, pp.145–158. WASD: London, United Kingdom.

The research regarding Deepfakes has been developing at a faster pace as technology to simplify the process becomes more accessible with the use of Artificial Intelligence (A.I.). Deepfakes are a part of the Fake News area of interest and, as such, have just as much impact on the current era of the Internet as the other parts that make up Fake News. This paper presents a survey ofUKUniversity Computer Science students (n = 179) and tests their ability to identify a deepfake video using their mobile phone devices. The results of the survey are able to demonstrate, with statistical significance, that educated university students in the field of Computer Science failed to identify Deepfake videos even when altered to the possibility that one of three videos is Deepfaked. In fact, while being altered, the respondents gave equal red flags to all the videos and those who indicated the correct sequence were statistically less accurate than if the guesses were made randomly. This contributes to an increasing call that educating the masses may not be enough in the fight against Fake News.

The Riva approach is used to develop an object-based Business Process Architecture (BPA) that helps in capturing the full organizational strategies. However, due to the lack of effective tools that can generate Riva BPA models from available artifacts, producing such models is time consuming non-automatic process and it requires an exhaustive manual validation to ensure the development of error-free models. This paper introduces a novel and domain-independent tool titled ‘ARivaT’ to automatically generate Riva BPA models from available knowledge assets such as Units of Work diagrams. ARivaT is underpinned by a step-by-step methodological approach that automates the process of generating Units of Work, First-Cut, and Second-Cut Process Architecture Diagrams. It also provides stakeholders with insightful explanations to allow a precise understanding of business processes and workflow. Furthermore, ARivaT employs a rule-based mechanism to seamlessly validate all the generated Riva BPA models. A case study-based approach has been followed to evaluate the applicability of ARivaT to derive Riva BPA diagrams. The results of our experiment are promising since 82 percent time saving has been recorded when using ARivaT to generate Riva BPA diagrams in comparison to using general drawing tools such as MS Word and MS Visio. In addition, 69 percent time saving has been noted when performing the same task with ARivaT as opposed to specialized tools such as Camunda.

The rapid technological developments have revolutionised approaches toward learning. The adoption of eLearning technologies such as chatbots has been increasing in the past few years, as there are various opportunities that can be identified to integrate educational chatbots with online learning process. For example, chatbots in education can provide various services such as personal tutoring, personal support, assessment and evaluation, etc. Iissues in remote learning—such as real-time assistance, feedback, and support—can be addressed by deploying educational chatbots. Yet, there are various challenges associated with chatbot technologies in education, e.g., novelty effect, cognitive load, the readiness of students and teachers, etc. This study reviews the various opportunities and challenges associated with educational chatbots in learning. These findings would help future researchers and designers to identify the core functionality and design aspects of educational chatbots, and also aids future research by the recommendations of research propositions.

Access to information has never been easier, thanks to the rapid development of the internet and communication technologies, and the ubiquity of smartphones and other internet-enabled devices. In traditional classroom learning, teachers provide students with various sources of information that are known to be reliable. Nowadays, especially in a post-pandemic era, students increasingly rely on a host of resources available on the internet. Exposure to vast amounts of scattered information could adversely affect students’ learning process. Meanwhile, pedagogical approaches, classroom learning practices, and student learning activities have evolved significantly to cope with contemporary challenges. This study reviews the current learning practices and the technological interventions in a rapidly evolving higher education landscape. In particular, the challenges when integrating technology into higher education are considered in detail and ways put forward for doing so in that context.

Blockchain technology has the potential to revolutionize several industries including finance, supply chain and logistics, healthcare, and more. This primer introduces readers to basic development skills to blockchain foundations including blockchain cryptography, the consensus algorithm, and smart contracts. Further, this primer explains stepwise how to implement and deploy basic data stores using blockchain with Python. The primer serves as a succinct introductory guide to blockchain foundations by relying on a case study illustrated with visuals together with instructions on implementation. This primer is intended for educators, students, and technology enthusiasts with foundational computer science and Python development skills.

House prices estimation has been the focus of both commercial and academic researches with various approaches being explored. Depending on the location, size, age, time and other factors, the value of houses may vary. This paper presents a modularized, process oriented, data enabled and machine learning based framework, designed to help the decision makers within the housing ecosystem to have more realistic estimation of the house prices. The development of the framework leverages the Design Science Research Methodology (DSRM) and the HM Land Registry Price Paid Data is ingested into the framework as the base transactions data. 1.1 million London based transaction records between January 2011 and December 2020 have been exploited for model design and evaluation. The proposed framework also leverages a range of neighborhood data including the location of rail stations, supermarkets and bus stops to explore the possible impact on house prices. Five machine learning algorithms have been exploited and three evaluation metrics have been presented and with a focus on RMSE. Results show that an increase in the variety of parameters enables improved accuracy which ultimately will enable decision making. The potential for future work based on this paper can explore the impact of the introduction of other groups of data on the accuracy of machine learning models designed for the estimation of house prices.

INCOM Egypt has undergone automation in some processes where critical aspects of its operations are transformed and automated. This paper presents an overview of INCOM Egypt processes using Ould Riva and analyses the process of ‘handling a product’. It aims to demonstrate effective automation of the production of wires and cables process accompanied to Industry 4.0 while considering environmental and economic sustainability goals that were inhibited by COVID-19 restrictions. Ould’s Riva method is used to analyse the production process of wires and cables to propose improvements for automating the process. Business process modelling is utilised to study the processes for clearer understating. The flow of information within the process is also analysed to integrate the production process with other processes and supply chains, which helps to identify which production activities can be automated and mainstreamed into the information flow to achieve environmental and economic sustainability. The context of INCOM Egypt, as a case study, is presented along with the Riva model of its operations. The paper identifies the before, i.e., As-Is process, and after, i.e., To-Be Process, automation of the ‘handle a product’ process using the Role Activity Diagram (RAD). The process involved redesigning and improving different activities to increase resource-use efficiency to participate in achieving the goals of sustainability. The focus of this paper is to investigate the negative impact of COVID-19 on sustainability and to examine the accomplishments of process automation of wire production towards environmental and economic sustainability. The results of the research reveal a relationship between business process modelling and sustainability. Moreover, automation of processes (Industry 4.0) is found to reduce the negative effect of COVID-19 on production. A triangulation between process modelling, process automation (Industry 4.0), and sustainability was determined. Each one is reinforcing and impacting one another. The RAD model demonstrates that automation of the activities in the process reduces waste, time, cost, and redundant processes as factors of sustainability, which may also help to lessen the unfavorable effects of the pandemic. The results proved generalisation on other organisations in the same line of business.

The COVID-19 disease caused by the SARS-CoV-2 infection has widely spread round the globe. Due to the large number of infected cases and rapid spread, it has been declared a global pandemic by World Health Organization on March 2020. There are several methods that identify and detect the COVID patient. However, detection using these methods can be confirmed after up to 10 days of the infection. This research presents a convolutional neural network (CNN) based classification model for detecting a COVID patient using CT image of patient. The dataset, used for the study, consists of CT images of variable sizes. It is a challenge for building a CNN model for variable sizes of the input image. This research uses a hybrid technique to overcome this challenge. It employs and analyses three different methods (such as Adam optimiser, Stochastic gradient descent with momentum optimiser, and RMSProp optimiser) for building the CNN model. Among the three CNN models, for CT image-based classification for infected or non-infected patient, adam performs better than RMSprop and sgdm. The classification accuracy achieved using adam is 94.9%, while RMSprop achieved an accuracy of 91.8% and sgdm reached 93.1%.

Recently many researches have explored the potential of visual programming in robotics, the Internet of Things (IoT), and education. However, there is a lack of studies that analyze the recent evidence-based visual programming approaches that are applied in several domains. This study presents a systematic review to understand, compare, and reflect on recent visual programming approaches using twelve dimensions: visual programming classification, interaction style, target users, domain, platform, empirical evaluation type, test participants' type, number of test participants, test participants' programming skills, evaluation methods, evaluation measures, and accessibility of visual programming tools. The results show that most of the selected articles discussed tools that target IoT and education, while other fields such as data science, robotics are emerging. Further, most tools use abstractions to hide implementation details and use similar interaction styles. The predominant platforms for the tools are web and mobile, while desktop-based tools are on the decline. Only a few tools were evaluated with a formal experiment, whilst the remaining ones were evaluated with evaluation studies or informal feedback. Most tools were evaluated with students with little to no programming skills. There is a lack of emphasis on usability principles in the design stage of the tools. Additionally, only one of the tools was evaluated for expressiveness. Other areas for exploration include supporting end users throughout the life cycle of applications created with the tools, studying the impact of tutorials on improving learnability, and exploring the potential of machine learning to improve debugging solutions developed with visual programming.

This chapter examines the buzzword “Fake News.” In recent years, politicians, media, and members of the public have used and misused the term, fake news, in a variety of contexts. This chapter focuses on the impact of fake news as it is linked to political participation through internet activism. An essential part of understanding what constitutes fake news is to appreciate the different characteristics and labels—Alternative Truth, Post Truth, Propaganda, Satire, and more—which leads readers vulnerable to the impact of fake news on a platform that requires little accountability for the facts or the harm it inflicts. The barriers to presenting a journalistic outlet as nothing less than a reputable news agency are only a few clicks away. In an era dominated by social media platforms, there is evidence that these networks inadvertently facilitated the propagation of fake news and their clickbait-driven profits.

Purpose Various technology-enhanced learning software and tools exist where technology becomes the main driver for these developments at the expense of pedagogy. The literature reveals the missing balance between technology and pedagogy in the continuously evolving technology-enhanced learning domain. Consequently, e-learners struggle to realise the pedagogical value of such e-learning artefacts. This paper aims to understand the different pedagogical theories, models and frameworks underpinning current technology-enhanced learning artefacts to pave the way for designing more effective e-learning artefacts. Design/methodology/approach To achieve this goal, a review is conducted to survey the most influential pedagogical theories, models and frameworks. To carry out this review, five major bibliographic databases have been searched, which has led to identifying a large number of articles. The authors selected 34 of them for further analysis based on their relevance to our research scope. The authors critically analysed the selected sources qualitatively to identify the most dominant learning theories, classify them and map them onto the key characteristics, criticism, approaches, models and e-learning artefacts. Findings The authors highlighted the significance of pedagogies underpinning e-learning artefacts. Furthermore, the authors presented the common and special aspects of each theory to support our claim, which is developing a hybrid pedagogical approach. Such a hybrid approach remains a necessity to effectively guide learners and allow them to achieve their learning outcomes using e-learning artefacts. Originality/value The authors found that different pedagogical approaches complement rather than compete with each other. This affirms our recommended approach to adopt a hybrid approach for learning to meet learners' requirements. The authors also found that a substantive consideration for context is inevitable to test our evolving understanding of pedagogy.

Increasing frequency of epidemics, such as SARS-CoV, MERS-CoV, Ebola, and the recent COVID-19, have affected various sectors, especially education. As a result, emphasis on e-learning and distance learning has been increasing in recent years. The growing numbers of mobile users and access to the internet across the world has created more favorable conditions for adopting distance learning on a wider scale. However, lessons learnt from current experiments have highlighted poor student engagement with learning processes, hence a user-centric approach to design and develop educational chatbots is presented. A User-centric approach enables developers to consider the following: learners’ and teachers’ technological skills and competencies, attitudes, and perceptions and behaviour; conceptual concerns, such as pedagogical integration on online platforms, assessment procedures, varying learning culture and lifestyles; technical concerns, such as privacy, security, performance, ubiquity; and regulatory concerns, such as policies, frameworks, standards, ethics, roles and responsibilities have been identified in this study. To address these concerns, there is the need for user-centric design and collaborative approaches to the development of distance learning tools. Considering the abovementioned challenges and the growing emphasis on distance learning, we propose chatbot learning as an effective and efficient tool for delivering such learning. In this regard, a user-centric framework for designing chatbot learning applications and a collaborative user-centric design methodology for developing chatbot learning applications is proposed and discussed.

This chapter examines the buzzword “Fake News.” In recent years, politicians, media, and members of the public have used and misused the term, fake news, in a variety of contexts. This chapter focuses on the impact of fake news as it is linked to political participation through internet activism. An essential part of understanding what constitutes fake news is to appreciate the different characteristics and labels—Alternative Truth, Post Truth, Propaganda, Satire, and more—which leads readers vulnerable to the impact of fake news on a platform that requires little accountability for the facts or the harm it inflicts. The barriers to presenting a journalistic outlet as nothing less than a reputable news agency are only a few clicks away. In an era dominated by social media platforms, there is evidence that these networks inadvertently facilitated the propagation of fake news and their clickbait-driven profits.

This In order to analyze the people reactions and opinions about Coronavirus (COVID-19), there is a need for computational framework, which leverages machine learning (ML) and natural language processing (NLP) techniques to identify COVID tweets and further categorize these in to disease specific feelings to address societal concerns related to Safety, Worriedness, and Irony of COVID. This is an ongoing study, and the purpose of this paper is to demonstrate the initial results of determining the relevancy of the tweets and what Arabic speaking people were tweeting about the three disease related feelings/emotions about COVID: Safety, Worry, and Irony. A combination of ML and NLP techniques are used for determining what Arabic speaking people are tweeting about COVID. A two-stage classifier system was built to find relevant tweets about COVID, and then the tweets were categorized into three categories. Results indicated that the number of tweets by males and females were similar. The classification performance was high for relevancy (F=0.85), categorization (F=0.79). Our study has demonstrated how categories of discussion on Twitter about an epidemic can be discovered so that officials can understand specific societal concerns related to the emotions and feelings related to the epidemic.

The rapid expansion of technologies in the education sector has led to the development of innovative pedagogical approaches being integrated with new technologies for enhancing the learning experience. Virtual assistants or chatbot technologies have been one of the primary focus in streamlining and enhancing learning processes by integrating pedagogic approaches with innovative technologies. This paper focuses on analyzing the recent developments in educational chatbots, as well as the identified issues in the design, development, and application of chatbots in e-Learning. Accordingly, a framework that reflects the various factors that need to be considered in chatbot design and developments in e-Learning is proposed and discussed in this paper.