As time goes on and the number of people who use information and communication technology (ICT) grows, emerging technologies are receiving a lot of attention from academics, researchers, and users. Emerging technologies are changing more quickly than ever, and it is important to start building skills early in education by using the right examples to teach and learn about digital citizenship. New pedagogies support individual teaching and learning methodologies for knowledge acquisition and self-directed learning. Hundreds of digital education tools have been developed to increase student autonomy, enhance academic process management, promote collaboration, and improve communication between teachers and students. This article examines the current state of emerging technologies from a variety of educational viewpoints, highlights a few of them, and discusses both their established and potential educational benefits. Additionally, it offers in-depth debates on recent developments and future challenges from a wide range of perspectives. The analysis focuses on peer-reviewed research articles published in well-renowned publications databases over the last 8 years, drawing upon a bibliometric analysis using VOSviewer. The results of this study are also anticipated to serve as a substantial impetus for other universities and colleges worldwide to utilize innovatively and revolutionized technology for better academic performance.

The emergence of Artificial Intelligence (AI) technology has significantly disrupted the educational landscape. The latest development in AI, generative AI that can generate new and tailored to specific content, has significantly impacted education. Given the value of AI technology in general and generative AI specific to users in education, such as students, the adaptability of these technologies has significantly increased. However, continuing and productive usage of AI tools depends upon students’ satisfaction with these tools. Drawing from the existing research, the present research has developed factors that affect students’ general satisfaction with AI tools. The research collected the data using a survey questionnaire from a Saudi Arabian university. The two-stage method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) have been employed. The two-stage method is applied in a way that PLS-SEM is used for testing the hypothesis and significance of the factor’s influence on satisfaction, and ANN is used to determine the relevant importance of the factor. The PLS-SEM results have shown that factors such as content quality, emotional wellbeing and perceived utility determine student satisfaction with AI tools. The ANN results show that emotional wellbeing is the most critical factor in satisfaction, followed equally by content quality and perceived utility.

Higher education institutions’ principal goal is to give their learners a high-quality education. The volume of research data gathered in the higher education industry has increased dramatically in recent years due to the fast development of information technologies. The Learning Management System (LMS) also appeared and is bringing courses online for an e-learning model at almost every level of education. Therefore, to ensure the highest level of excellence in the higher education system, finding information for predictions or forecasts about student performance is one of many tasks for ensuring the quality of education. Quality is vital in e-learning for several reasons: content, user experience, credibility, and effectiveness. Overall, quality is essential in e-learning because it helps ensure that learners receive a high-quality education and can effectively apply their knowledge. E-learning systems can be made more effective with machine learning, benefiting all stakeholders of the learning environment. Teachers must be of the highest caliber to get the most out of students and help them graduate as academically competent and well-rounded young adults. This research paper presents a Quality Teaching and Evaluation Framework (QTEF) to ensure teachers’ performance, especially in e-learning/distance learning courses. Teacher performance evaluation aims to support educators’ professional growth and better student learning environments. Therefore, to maintain the quality level, the QTEF presented in this research is further validated using a machine learning model that predicts the teachers’ competence. The results demonstrate that when combined with other factors particularly technical evaluation criteria, as opposed to strongly associated QTEF components, the anticipated result is more accurate. The integration and validation of this framework as well as research on student performance will be performed in the future.

In recent years, mixed reality (MR) technology has emerged as a promising tool in the field of education, offering immersive and interactive learning experiences for students. However, there is a need to comprehensively understand the impact of MR technology on students’ academic performance. This research aims to examine the effect of mixed reality technology in the educational setting and understand its role in enhancing the student’s academic performance through the student’s novel learning experiences and satisfaction with the learning environment. The present research has employed a quantitative research design to undertake the research process. The survey questionnaire based upon the five-point Likert scale was used as the data collection instrument. There were 308 respondents studying at various educational institutes in Saudi Arabia, all of whom were using mixed reality as part of their educational delivery. The findings of the present research have indicated that the application of mixed reality by creating experiential learning, interactivity and enjoyment can significantly enhance the student’s novel experience, which can directly enhance students’ satisfaction with learning objects and the learning environment, as well as indirectly enhancing the student’s academic performance. The research offers various kinds of theoretical implications and policy implications to researchers and policymakers.

Enhancing student academic performance remains a critical challenge for educators and administrators. Among various interventions, gamification has gained increasing attention as a promising approach to improving learning outcomes. However, existing research on the role of gamification in education remains fragmented due to its multidisciplinary nature. This study aims to synthesize current knowledge through a systematic review in order to develop and validate a conceptual framework linking gamification to student academic performance. By following the PRISMA framework, 62 relevant studies were reviewed, highlighting several recurring themes, including game design and development, student performance outcomes, and critical aspects of gamification such as cognitive development, motivation, and empowerment. Key mechanisms identified include active learning, personalized and adaptive learning, and collaborative interaction. Reported outcomes of gamification interventions include higher test scores, reduced anxiety and stress, and increased engagement and positive attitudes toward learning. Building on these findings, the resulting conceptual framework was validated through empirical research. Data were collected from 289 students from Saudi Arabia using a structured survey instrument, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for analysis. The results provide empirical support for the proposed framework, confirming gamification as a significant driver of improved student academic performance. The findings provide practical implications for educators and policymakers seeking to leverage gamification as a strategic tool for enhancing student learning outcomes.

Since the evolution of massive online open courses (MOOCs) as an Ed-Tech solution to various educational problems, learners have registered themselves for various MOOCs offered by various universities and MOOC platforms. However, it has been observed that many learners who register need to complete the course and progress further. Thus, the present research aims to study the learners’ lack of continuance of MOOCs. The research is based upon a quantitative research design in which a conceptual model is developed and tested empirically by employing a survey questionnaire as a tool for data collection. The data was collected from 377 respondents who were university students from Saudi Arabia studying at Jazan University, and partial least square-structural equation modeling (PLS-SEM) was used as a tool for data analysis. The results of PLS-SEM show that learner continuance behavior depends on three elements: perceived career success, perceived training opportunity, and satisfaction with MOOCs. The results further show that content vividness, interactivity, and intellectual curiosity are antecedents of satisfaction with MOOCs. The present research has argued that user gratification will yield continuance with technology products. It argues that rational aspects such as career success and acquisition of tangible skills can also yield continuance with technology products and services.