ABSTRACT. Artificial Intelligence (AI) in recent years has shown an unprecedentedly impres-sive development, tending to play a catalytic role in all aspects of life. The interest of the academic community, but also of governments, is huge in the dynamics of AI and is reflected by the truly explosive amount of investment and research that is underway. Enthusiastic opinions and statements about AI are made every day, but at the same time they also bring to the fore alarming predictions about its ef-fects. This paper aims to describe the opportunities emerging from the use of arti-ficial intelligence and ChatGPT to improve education, but also to provide an early assessment of ChatGPT in education.

ABSTRACT. As the concept of the Metaverse gains traction, its potential to revolutionize edu-cation has become increasingly evident. This abstract explores the role of ChatGPT, an advanced AI-powered language model, in enhancing the Metaverse's impact on education. The Metaverse represents a digital convergence of physical and virtual realities, fostering immersive and interactive environments. ChatGPT's integration into this space introduces a novel dimension to education, redefining how students and educators engage with content and each other. This abstract delves into ChatGPT's contributions to the educational landscape within the Metaverse. By harnessing natural language processing capabilities, ChatGPT enables real-time, contextually rich interactions. It acts as an intelligent conversa-tional agent, offering personalized guidance, answering queries, and facilitating discussions. This interactivity engenders a dynamic learning experience, trans-cending the boundaries of traditional classrooms. Additionally, ChatGPT's lan-guage versatility ensures inclusivity in education. It bridges linguistic gaps, ena-bling multilingual interactions and content delivery. The AI's role as a virtual tutor and content creator further strengthens its significance. From explaining complex concepts to generating tailored learning materials, ChatGPT facilitates adaptive learning experiences. Furthermore, ChatGPT's continuous learning analytics fur-nish educators with insights into student behaviors, informing data-driven in-structional strategies. Collaborative learning is promoted through the AI's media-tion of discussions and group projects. As a virtual companion, ChatGPT nur-tures personalized, immersive, and collaborative learning environments, empow-ering learners within the Metaverse.

ABSTRACT. In the evolving landscape of digital communication, sentiment analysis provides crucial insights into public opinions, particularly concerning emerging technologies like the Metaverse. While various models have been employed to perform sentiment analysis, there is a need to assess the effectiveness of traditional machine learning approaches, specifically Logistic Regression (LR), given its advantages in terms of simplicity and interpretability. This study employs LR to analyze a dataset of tweets related to the Metaverse, focusing on preprocessing techniques such as tokenization and vectorization to optimize model performance. The LR model demonstrated high efficacy, achieving an accuracy of 96% with precision, recall, and F1-scores of 0.93, 0.96, and 0.95 respectively for negative sentiments, and 0.97, 0.95, and 0.96 for positive sentiments. The findings suggest that Logistic Regression remains a robust tool for sentiment analysis in social media contexts, offering significant implications for businesses and developers interested in the public perception of new technologies such as the Metaverse.

ABSTRACT. This study explores the hypothesis that students learn better when engaged in activities where they validate ChatGPT responses by contrasting them to reliable and valid human-generated content material. By applying an ecologically valid but not controlled experimental design we asked students to individually choose to work on an assignment either before (N=80) or after (N=42) a course written examination session. The assignment included four scenarios guiding students to interact with ChatGPT and evaluate afterwords the validity of the obtained responses. Available data indicate that students who worked on the assignment prior to examination were able to provide significantly improved answers to examination items that were conceptually relevant to the assignment tasks as compared to those irrelevant. This outcome, however, was valid only for open-ended questions included in the examination sheet and not for the closed-type ones. Overall, this study provides concrete research evidence that ChatGPT-like AI tools can provide the basis for designing beneficial learning activities, assuming the role of a “less competent partner” and offering to students the opportunity of critically reviewing their generated content. At theoretical level, we discuss how this perspective is in line with the proposed “AI as a black box” approach which bypasses the issues relevant to possible errors and misguidance generated by current level conversational AI technologies.

ABSTRACT. In the evolving landscape of education, the integration of generative intelli-gence (GI) promises significant enhancements in the pedagogical processes within mass communication and management studies. This paper examines the transformative impact of advanced artificial intelligence technologies, including Generative Adversarial Networks (GANs) and Generative Pre-trained Transformers (GPTs), on educational assessments. By facilitating dynamic and interactive learning environments, GI tools such as large lan-guage models extend educational boundaries and foster a continuous, adap-tive interaction among students, educators, and AI systems. These technol-ogies not only personalize learning experiences but also support the devel-opment of critical thinking skills through tailored content and feedback mechanisms. However, the deployment of such AI-driven innovations ne-cessitates a cautious approach, addressing ethical concerns related to data privacy, algorithmic bias, and the integrity of AI outputs. This study ex-plores both the potential and challenges of GI, providing a balanced per-spective on its role in enhancing educational outcomes and shaping future educational practices.

ABSTRACT. The great development of technology and in particular artificial intelligence (AI) is of great importance in development in all fields and sectors, including university education, as it is an innovative means that contributes to the development of learning processes and opens new horizons and tremendous opportunities for improvement and enhance the efficiency of university education. This study provides an exploratory survey to explore the role of (AI) in improving university education from the point of view of Arab university professors and how this affects the learning experience of students and researchers. It also highlights the challenges facing the use of (AI) in teaching and learning processes from the point of view of professors and identifying the proposed solutions to overcome challenges and promote the use of (AI) technologies.

ABSTRACT. Generative Pretrained Transformers (GPT) have significantly improved natural language processing, showcasing enormous versatility across diverse applications. Although GPT models have enormous potential, they frequently encounter issues such as mistakes and hallucinations, which may limit their practical use. Addressing these shortcomings, Retrieval-Augmented Generation (RAG) represents an innovative approach that potentially enhances the accuracy and reliability of these models by leveraging external databases to correct and enrich their outputs. In our study, a RAG-augmented GPT-4 model was tested within an AI-focused Massive Open Online Course (MOOC) and outperformed a standard GPT-4 model, achieving an 85% success rate compared to 81%. Notably, it also surpassed the average student performance, underscoring its ability to deliver precise and contextually relevant responses. These findings confirm the efficacy of RAG in enhancing AI models for educational use and suggest that educators can leverage this technology to refine assessment methods and create more engaging, personalized learning experiences for students.

ABSTRACT. The most important aspect of an educational assessment is related to the closeness of the results to the actual knowledge level of the assessee. In this way, the fidelity of the assessment is ensured. In this matter, this paper presents the description and potential results of a model that determines a recognition of a detailed knowledge report related to the assessment topic, including the situation of partial knowledge. Thus, a model that details the usage of genetic algorithms (GAs) for establishing a method of partial scoring using fuzzy logic and weights given to specific parts of the assessment items is presented in this paper. Shortly, assessment items such as MCQ (multiple-choice) or cloze questions are given as examples of situations where this method of partial scoring can be applied. The genetic algorithm is used to generate the optimal scoring weights to the option of the assessment items related to a given value of entropy or scoring equilibrium between the scores chosen for the items. The purpose of this analysis is to obtain an optimal score configuration for an item which can offer chosen score equilibrium between the options score and which could give the opportunity of partial scoring, which has multiple benefits for the assessment process.

ABSTRACT. This paper examines the usage of genetic algorithms for performance analysis and optimization in distributed web systems. The selected system for distributed systems performance analysis pertains to educational assessment domains. Genetic algorithms (GA) offer a promising approach for automating the optimization process. In this study, the GA is used to generate educational assessment tests within an educational framework. The assessment tests are formed of items stored in data structures and used in the generation process. This process is delimited by several requirements of the assessment objectives, such as the degree of difficulty or solving time. A typical GA approach leads to optimised sequences of items or tests, whereas the usage of distribution within the process can surpass usual GA issues, such as local optimisation. The distribution consists in the generation of the assessment tests on several nodes within a network, where each node has generative tasks. Moreover, they have been successfully applied and solved a wide range of problems, including scheduling, routing, and load balancing in distributed systems. This article also presents a comparative analysis of GA performance: centralized GA and distributed GA. Centralized GAs run on a single computer, while distributed GAs run on multiple computers. This work represents an important step in the understanding and application of genetic algorithms in the context of distributed web systems, providing a solid foundation for future research in this area. The conclusions of this paper demonstrate the effectiveness of genetic algorithms in developing the performance of distributed systems.

ABSTRACT. ABSTRACT—science, technology, engineering, and mathematics represent powerful educational methods and concepts, which is STEM education. STEM is the new paradigm shift in the education system that enables students to learn real-life scenarios and the proper thinking process to solve real-life problems requiring using integrated concepts. This type of learning improves students’ 21st-century skills to make decisions, solve real-life problems, lifelong learning, innovate, and to think creatively and critically in solving problems. STEM education is new in the United Arab Emirates, and there are no or few types of research applied to it. The main purpose of this paper is to propose a framework for STEM education to be taught in UAE schools that meets the objectives of the strategic action plan (2015-2021) of the Ministry of Education MOE. The introduced framework is structured in four levels. Each education level performs a hierarchy of relative difficulty, complexity, and depth. Going higher in the hierarchy of the qualification’s framework levels means excellent challenges will be faced, advanced knowledge and skills are required, and high demand is expected of a student.

ABSTRACT. The precise identification of pathogens in biological material is critical for appropriate medical diagnosis and therapy. Medical laboratory students must be proficient in laboratory skills since they play a critical part in the diagnostic process. Using appropriate microscope techniques, one must be able to identify a wide range of pathogens, including bacteria, viruses, parasites, and fungi. Traditional methods of skill development include on-campus practical lessons and field training in hospital microbiology departments. However, the emergence of generative artificial intelligence (AI) opens new avenues for educational enhancement. This study investigates the feasibility of using generative AI, specifically Gemini, to train medical laboratory students in bacterial identification using morphological traits seen in micrographs. The study assessed student learning results using Gemini-generated case studies and quizzes. The results showed that Gemini-generated quizzes helped pupils identify different bacteria based on the micrographs supplied. However, limitations were identified, such as the requirement for teachers to manually add photographs. Overall, the study highlights the potential of generative AI tools in educational contexts, arguing that they could supplement traditional teaching techniques and improve the learning experience for medical laboratory students. More research into generative AI's educational applications is needed to fully realize its potential in medical education.

ABSTRACT. Intelligent attendance systems play an essential role in enhancing human well-being within cognitive education frameworks. These systems include modern technology to facilitate attendance tracking processes, minimizing administrative burdens and maximizing instructional time. By automating attendance-related tasks, educators can allocate more resources towards personalized learning experiences addressed to individual cognitive needs. Moreover, intelligent attendance systems enforce a sense of accountability and punctuality among learners, instilling valuable time-management skills essential for academic and professional success. Beyond efficiency gains, these systems contribute to a positive learning environment by providing trust and transparency between students and educators. Finally, by optimizing attendance management, intelligent systems allow educators to focus on student development, enforcing a conducive atmosphere for cognitive growth and well-being within educational settings. In this paper, we explore intelligent attendance systems that are proposed for use in a variety of schools, universities, and other educational institutions that aim to enhance cognitive education.

ABSTRACT. Artificial Intelligence (AI) has increasingly become a pivotal technology in transforming various sectors, including education and personal well-being. This study explores the multifaceted role of AI in these areas, highlighting its potential to personalize learning experiences, enhance accessibility, and support mental and physical health. The study identifies key opportunities where AI can be integrated into educational settings to tailor learning paths according to individual student needs and capabilities, thereby promoting more effective learning outcomes. In the realm of well-being, AI's capability to monitor mental health through predictive analytics and provide personalized health recommendations showcases its transformative potential. However, the deployment of AI technologies also brings forth significant challenges, particularly concerning data privacy, ethical considerations, and the risk of increasing social inequalities. The findings underscore the necessity for robust ethical frameworks and equitable access to AI technologies to ensure they benefit all sectors of society. Recommendations are provided for educators, healthcare providers, and policymakers to navigate the complexities of AI integration, emphasizing the importance of collaboration among interdisciplinary teams to harness AI's potential responsibly.

ABSTRACT. The Transformer model has inspired state-of-the-art generative NLP models such as the Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT) and their variations models. Despite the fact that these training methods are responsible to have an effective language model, but the computational cost will be very expensive for performing any NLP tasks. Accordingly, the fine-tuning plays an essential role in improving the performance during the training process and reducing the computational cost. In this study we applied one of the pre-trained models from HuggingFace platform called a BERT-base-uncased model, which it’s variant of BERT and we utilized a specific purpose dataset for high school advising. However, the data is collected from high school and universities websites as well as from educational experts. The data includes enquiries and answers about advising high school students toward their future. This transformer takes the input as a pair from the context and the question, and the output defined with the start and end positions of the answer in the context. Accordingly, the collected dataset is converted into json file, and then we applied the PyTorch libraries for building both, training and inference models. The ROUGE metrics revealed that the model achieves a good performance in answering to the students’ questions.

ABSTRACT. Generative AI is a popular and revolutionary tool in the educational industry which helps the schools to shift from the traditional teaching methodology and redesign their classrooms. This research is focused on the identification of gaps in the practical application of generative AI in educational settings and the evaluation of the effect this has on students’ learning outcomes and social sustainability. The main objectives of this research are to review the variables that play a significant role in students' use of generative AI technologies and to note some findings that are not yet well covered in the educational literature. The objectives also include the development of a theoretical model which integrates the UTAUT2, UGT and SDT with hypothetical generative AI concepts. This model will be further verified by a Hybrid SEM-ANN approach to guarantee the comprehensive analysis of the complex patterns and the relationships. Moreover, the Interpretive Structural Modeling (ISM) method will be utilized to reveal the major interconnections among the factors of the selected theories that were identified by the SEM-ANN analysis; this will help to have a general picture of the relationship among the different variables. Consequently, the paper will be useful not only for students’ learning outcomes but also for educational literature, teaching methods, and reforms in education.

ABSTRACT. In the wake of global shifts towards digital platforms, online education has become a cornerstone of modern learning environments. Understanding student adaptability in these settings is crucial for developing effective educational strategies and ensuring successful learning outcomes. While the transition to online education offers numerous benefits, it also poses significant challenges, particularly in terms of student engagement and adaptability. Identifying factors that influence adaptability can help educators tailor interventions to assist students who may struggle with online learning modalities. This study utilized a dataset from Kaggle, consisting of 1,205 students with features encompassing demographic information, technological access, and personal educational environments. A Random Forest classifier was employed within a One-vs-Rest strategy to predict three levels of student adaptability to online education. The model's performance was evaluated using accuracy, precision, recall, and F1-score metrics. The Random Forest model achieved an accuracy of 88.3%. It showed high precision and recall for the 'High' and 'Moderate' adaptability classes but lower performance in predicting 'Low' adaptability. The analysis also revealed that class duration, financial condition, and age were among the most significant predictors of adaptability.The findings underscore the potential of machine learning in identifying key factors affecting student adaptability, which can inform the design of personalized learning experiences and interventions in online education. These insights are pivotal for educational institutions aiming to enhance student engagement and reduce dropout rates in digital learning environments.

ABSTRACT. Student retention is a critical concern for higher education institutions worldwide, impacting both institutional success and student outcomes. High dropout rates can lead to significant financial losses for universities and detrimental effects on students' personal and professional futures. Predicting student retention accurately enables institutions to proactively address factors leading to dropouts and implement targeted interventions to support at-risk students. This study addresses the problem of student retention prediction by leveraging advanced machine learning techniques. Specifically, we utilized a RandomForestClassifier to analyze a comprehensive dataset of student records, which includes various features related to demographics, academic performance, and other relevant factors influencing student retention. Our methodology involved several steps: data preprocessing to encode categorical variables and scale numerical features, hyperparameter tuning using GridSearchCV to optimize the model, and evaluation of the model's performance using metrics such as accuracy, precision, recall, F1-score, and ROC curves. Visualizations were generated to provide deeper insights into the model's performance and behavior. The results of our analysis indicate that the RandomForestClassifier can effectively predict student retention, achieving an accuracy score of 76.72%. This performance demonstrates the model's potential as a valuable tool for higher education institutions aiming to improve student retention rates. By integrating such predictive models into their student support systems, universities can identify at-risk students early and provide targeted support to enhance their chances of success. This proactive approach can lead to better academic outcomes for students and reduced financial losses for institutions due to dropouts. Future research could explore the integration of additional features and alternative machine learning models to further improve predictive accuracy and applicability in diverse educational contexts.

ABSTRACT. The integration of artificial intelligence in educational settings has increasingly shown potential to enhance the efficiency and accessibility of information. One such application is the development of chatbots that can provide instant responses to frequently asked questions, thereby alleviating the workload on administrative staff and improving user experience. This paper presents the development and evaluation of a chatbot designed to assist students and staff at a university by providing accurate responses to common queries. Using a dataset sourced from Kaggle, the chatbot is trained on various intents, including course information, fees, hostel facilities, and more. The dataset undergoes thorough preprocessing, including tokenization, lemmatization, and vectorization to ensure effective model training. The neural network model is built using TensorFlow and comprises multiple dense layers with ReLU activation functions and dropout layers to prevent overfitting. The model is trained over 200 epochs with a batch size of 5, utilizing the Adam optimizer and categorical cross-entropy loss function. The results demonstrate the chatbot's high accuracy and effectiveness, achieving an accuracy of 99.75%, precision of 99.76%, recall of 99.75%, and an F1 score of 99.75%. These metrics indicate the model's robustness in understanding and responding to user queries accurately. The implications of this study suggest that implementing such chatbots in educational institutions can significantly streamline information dissemination and improve user engagement.

ABSTRACT. The prediction of student performance is a critical area of research in educational data mining, aiming to identify factors that contribute to academic success or failure. Accurate prediction models can help educators and policymakers develop interventions to improve student outcomes. Despite the availability of various machine learning techniques, there remains a need for a comprehensive comparison of these methods applied to a single dataset. This study addresses this gap by applying several classical machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, and Gradient Boosting, to predict student performance using a publicly available dataset from Kaggle. The dataset includes demographic and educational attributes such as gender, race/ethnicity, parental level of education, lunch type, and test preparation course. The models were evaluated based on accuracy, precision, recall, F1 score, and ROC curves. Logistic Regression emerged as the top-performing model with an AUC of 0.72, accuracy of 0.71, and F1 score of 0.81, indicating robust predictive capability and high interpretability. Naive Bayes also performed competitively, highlighting the effectiveness of simple probabilistic models. These findings provide valuable insights for educators and policymakers, emphasizing the importance of machine learning in early identification of students at risk of underperforming, thereby enabling timely interventions. Future research should explore advanced techniques such as deep learning, feature engineering, and longitudinal data analysis to further enhance predictive accuracy. Additionally, the interpretability and ethical implications of deploying these models in educational settings must be considered to ensure responsible usage.

ABSTRACT. In the highly competitive landscape of academic institutions, there are numerous challenges, including the delivery of high-quality education, the establishment of effective systems for assessing student performance, and the anticipation of future educational needs. As academic institutions undergo a paradigm shift with the computerization of data management, there is a growing interest among educational stakeholders in exploring innovative machine learning applications(ML) in educational Data Analysis. This comprehensive review aims to critically assess and synthesize the existing literature on the application of ML in educational contexts. The primary focus is on uncovering prevalent trends and successful techniques within this domain. The review delves into the ML algorithms, particularly their efficacy in predicting academic performance, fostering personalized learning experiences, and influencing overall educational outcomes. Identifying research gaps is a key objective, along with proposing potential avenues for future studies to propel the integration of machine learning in educational settings. The review addresses four primary research questions: common ML approaches and algorithms in educational data analysis, the significance of features in ML models, types of educational data frequently employed in research, and the educational outcomes and improvements associated with ML implementation. By exploring these dimensions, the review aims to contribute substantial insights into the evolving landscape of ML in education, offering a roadmap for future research endeavors in this dynamic field