In higher education institutions, effective digital communication is crucial for achieving administrative goals, such as improving student services, managing resources, and facilitating collaboration among staff members. By exploring the impact of copresence factors on digital communication effectiveness, higher education institutions can gain a deeper understanding of the factors that influence their digital communication and develop strategies that optimize its efficiency. The study applied a quantitative research approach through a questionnaire survey to collect required responses from employees who are working in the higher education institutions of Jordan with a total of 304 participants. The findings of this study indicate that copresence factors play a significant role in the effectiveness of digital communication within higher education institutions in Jordan. The results support the framework developed by others and suggest that self-copresence and partner-copresence have a positive impact on the efficiency of communication. This highlights the importance of considering the presence of individuals during digital communication and the impact it can have on the quality of the exchange. In conclusion, the study sheds light on the importance of correspondence in digital communication and its impact on the efficiency of communication within higher education institutions. The findings can help in the development of strategies and practices for enhancing the effectiveness of digital communication and improving administrative work in higher education institutions in Jordan.

The spread of the Covid-19 pandemic and the interruption of personal communication between the teacher and students in higher education led to the need for finding solutions that enable the continuation of the educational process and ensure access to accurate information that improves the level of human capital in dealing with dynamic environments. Therefore, this research sought to analyse the impact of the application of artificial intelligence in entrepreneurship education in Qatari higher education institutions after the Corona pandemic. The measurement of artificial intelligence was based on dimensions (machine learning, natural language processing, expert systems, and machine vision), while entrepreneurship education was measured by dimensions of (entrepreneurial cognition, entrepreneurial competence, and innovation spirit). The research followed an experimental quantitative approach based on collecting data from Qatari university students using a questionnaire developed for the research purpose. Hence, the convenience sample used in the research was composed of 402 students from various Qatari universities, which represents a response rate of 67% from the distributed questionnaires. The statistical analysis of the research data was based on the covariance-based structural equation modeling technique (CB-SEM). The results of the research indicated that all dimensions of artificial intelligence had a positive impact on entrepreneurial education, with the highest impact being machine vision and the lowest impact being natural language processing. Accordingly, the results of the research revealed the need to invest in technological capabilities for supporting the educational system aimed at generating innovative human resources capable of coping with the uncertainty of the work environment.

E-learning results from the integration of technology and education and has become an effective learning medium today. E-learning courses and systems with various services are on the rise owing to its importance. E-learning systems should be evaluated to assure successful delivery, effective usage, and positive impacts on learners. A holistic model that identifies various levels of success on a vast range of success determinants was proposed. The model was empirically validated using data obtained from 724 e-learning student users in Jordan. Structural Equation Modelling (SEM) was used in data analyses. Results showed that perceived usefulness of information systems, user training, system quality, and management support have positive effects on user’s behavioral intention; whereas perceived ease of use has not. Also, SEM displayed that user behavioral intention has a positive effect on information systems use, use on student satisfaction, and the latter on student loyalty. Machine Learning (ML) methods produce high correlation values reaching up to 80% in predicting Behavior Intention (BI) from the input factors, and student loyalty from student satisfaction factors. This indicates that the ML are promising techniques to forecast the future targets based on the input independent features.