Educational institutions are adopting learning management systems (LMS) to facilitate teaching and learning processes. During the last few years, many Universities have started upgrading their existing LMS by shifting to advance LMS. This shift requires students, academic as well as administrative staff to get acquainted with the functioning of the new system at the earliest, as any change in the system may impact their performance. The transition from old to new LMS requires time and affects the performance of users, especially administrative staff performance. The present study tries to investigate the effect of the transition on the performance of the administrative staff. The task-technology fit (TTF) model was adopted as the theoretical framework for the study. The data analysis was done using the PLS-SEM, to test the hypothesized relationships. The findings of the study confirm that mere usage of the new technology did not improve the performance rather, the task and technology characteristics need to be coordinated appropriately.

Mobile learning application now is considered a powerful application for learning and was adopted in universities in the period of Covid-19. After Covid-19 pandemic, university students have been allowed to use mobile learning systems, it is needed to ensure students’ intention to continuously use mobile learning for their learning activities or not. Thus, the purpose of this paper is to understand the main determinants that encourage the continuous use of mobile learning. To achieve that, we used the UTAUT-2 model to predict the main determinants of mobile learning acceptance. In our study, a quantitative technique was employed to collect the data. A hybrid approach SEM-ANN was applied to validate the research model. The findings indicated that performance expectancy and effort expectancy had a strong effect on students' attitudes towards mobile learning platforms. In addition, the results showed that performance expectancy and effort expectancy have a significant impact on students' continuous intention to use mobile learning platforms after Covid-19. In addition, hedonic motivation and habit had a positive effect on both students' attitudes and continuous intention to use mobile learning platforms. Moreover, Social influence factor and facilitating conditions had a significant effect on students' continuous intention to use mobile learning platforms after Covid-19.

Advancements in technology have had a profound impact on the way we learn, teach, and access knowledge. From online learning platforms to interactive educational games and virtual reality simulations, technology has transformed the traditional classroom into a dynamic, engaging, and inclusive space for education. One of the promising advancements in the field of artificial intelligence technology is ChatGPT which offers personalized and effective learning experiences by providing students with customized feedback and explanation. The effect of ChatGPT must be compared with the effect of Google at the educational level since both present a source of information and explanation. Thus, this study aims at investigating the differences between these two learning sources to measure their effectiveness from different perspectives. The model proposed in this study was evaluated using the PLS-SEM approach, utilizing data collected from 153 university students in the UAE. The results of this evaluation indicate that the GPT (Generative Pre-trained Transformer) has a significant impact on user acceptance, mediated by information quality, system quality, perceived learning value, and perceived satisfaction. These factors play a crucial role in determining users' acceptance of the GPT. However, it is important to note that some aspects of the model were not supported, suggesting that they do not have a significant predictive effect on the use of ChatGPT. Nonetheless, the findings of this study contribute to the existing literature on AI and environmental sustainability, providing valuable insights for practitioners, policymakers, and AI product developers. These insights can help guide the development and implementation of AI technologies in a way that aligns with users' needs and preferences while considering the larger environmental context.

With flexible learning, students are actively engaged in their own education and are held to high standards of performance. Online academic courses make it easier for students to receive personalized education because they provide students with more flexibility to concentrate on what is most important to them and give them greater control over their own education. This study’s objective was to investigate whether there is a correlation between how well students succeed in online classes and the extent to which they make use of the schedule and the geographical and resource flexibility offered by such programmes. This article uses a developing approach for predicting and classifying the flexibility in online learning of students who are at risk of failing due to academic and demographic variables. The K-nearest neighbours (KNN) method, the random forest (RF) method, and the logistic regression method were used to categorise the students participating in flexible online learning. The information for the dataset came from Kaggle, and it was gathered for use in testing machine learning. The dataset had a total of 1,875 instances representing 11 different features. Also, accuracy, precision, sensitivity and f-score metrics were applied to evaluate the system. The results show that the RF algorithm has a high accuracy percentage of 85%. The empirical findings demonstrate that students formed distinct patterns of learning time, location and access to knowledge. This suggests that flexibility was used to a significant degree. Patterns in learning time and the availability of learning materials were shown to have a substantial relationship with the accomplishments of the students. Understanding flexibility use habits may help adapt lessons and boost collaboration among similar students.

Promoting sustainability development in education is a global endeavor, aiming to foster the sharing of experiences and knowledge on sustainability development. To achieve that, educational institutions worldwide have increasingly embraced educational technology and integrated online learning components into their instructional methods. This research focuses on the pivotal role of students as influential catalysts for advancing sustainable development within higher education. Specifically, it investigates the extent of students' familiarity with sustainable development initiatives within higher education institutions in the UAE. To achieve this objective, the study introduces the Technology-Integration Framework for Education Sustainable Development (TIFESD), which serves as an evaluative tool for appraising students' awareness of technology-driven elements woven into the broader context of Education for Sustainable Development (ESD) within their respective universities. The research employs a quantitative methodology, encompassing the collection of 513 survey responses from students across nine universities in the UAE. This data analysis explores the potential relationship between the integration of technology and students' cognizance of factors that bolster sustainable development. The study's outcomes underscore students' profound awareness of a spectrum of technology-driven elements, including Green Campus initiatives, Smart Education strategies, Smart Campus facilities, and the influence of curriculum and course offerings—all of which collectively contribute to the advancement of sustainable development practices within higher education institutions.