Using adaptive learning technologies, personalized feedback, and interactive AI tools, this study investigates how these tools affect student engagement and what the mediating role of individuals’ digital literacy is at the same time. The study will target 500 students from different faculties such as science, engineering, humanities, and social sciences. With the changing trends in educational technology, it is important to know if these tools allow students to interact with learning materials. Through this study, we explore how adaptive learning technologies, which adapt content to students’ progress, are influenced by student motivation and participation during the learning process using AI tools that provide real-time feedback and interaction. Also, digital literacy is presented as a moderating factor that may either accelerate or impede the effectiveness of these tools. These findings demonstrate that more adaptive learning technologies, which have organized feedback, and interactive AI tools help improve student engagement. Additionally, students with higher levels of digital literacy are more involved with digital tools. This research recognizes that teachers should incorporate these technologies into their courses in such a manner as it synergizes with student’s digital capabilities to reap the benefits of technology on students’ engagement and learning outcomes.

Purpose: The purpose of this paper is to investigate the effect of service quality on graduates’ satisfaction as perceived by Middle East University (MEU) graduates.Design/methodology/approach: This research is cross-sectional and aims to explore the effect of service quality dimensions (academic staff, administration, classrooms and library services) on graduates’ satisfaction. Data were collected from 399 graduates. After confirming validity, reliability and normality of the data, and the correlation between variables, multiple regressions were used to test the hypothesis.Findings: The results show that all service quality dimensions are highly implemented by the MEU. The relationships between all service quality dimensions and graduates’ satisfaction are strong. Finally, results show that all service quality dimensions affect graduates’ satisfaction.Research limitations/implications: To generalize the results of this research, further studies are recommended to be carried out on other universities especially in Jordan. Testing the perception and satisfaction of other universities, stakeholders will help to improve service quality and to gain suitable competitive strategies.Practical implications: Service quality is a key driver for universities’ sustainable competitive advantage; therefore, dimensions of service quality should be included within universities plan, strategies and daily activities.Social implications: Considering service quality in higher education improves countries’ economic development, quality of life and well-being. All corporate social responsibility pillars (social, economic, environmental responsibilities and national and international regulation and norms) should be adapted and adopted within services quality systems and programs.Originality/value: Most of previous studies were carried out to test the students’ perception while this research is dedicated to explore graduates’ perception regarding service quality offered by the MEU.

Predicting the outbreak of a pandemic is an important measure in order to help saving people lives threatened by Covid-19. Having information about the possible spread of the pandemic, authorities and people can make better decisions. For example, such analyses help developing better strategies for distributing vaccines and medicines. This paper has modified the original Susceptible-Infectious-Recovered (SIR) model to Susceptible-Immune-Infected-Recovered (SIRM) which includes the Immunity ratio as a parameter to enhance the prediction of the pandemic. SIR is a widely used model to predict the spread of a pandemic. Many types of pandemics imply many variants of the SIR models which make it very difficult to find out the best model that matches the running pandemic. The simulation of this paper used the published data about the spread of the pandemic in order to examine our new SIRM. The results showed clearly that our new SIRM covering the aspects of vaccine and medicine is an appropriate model to predict the behavior of the pandemic.

This paper addresses the conceptual exploration of various issues that form an integral part of the ethical dimensions surrounding the use of artificial intelligence (AI) in the field of marketing. We critically review some of the main ethical challenges AI poses to humanity: privacy, data security, bias, transparency, and accountability. Therefore, we will discuss the delicate balance between exploiting the transformation potential of AI for personally designed marketing strategies and ethical imperatives to safeguard the rights of consumers to retain trust. Then, the present regulatory landscape responding to these ethical challenges will be assessed by building on effective regulation, like the GDPR, existing proposed legislative frameworks, and industry guidelines. To underscore the role of an ongoing dialogue between marketers, technologists, ethicists, and regulators for developing a responsible AI ecosystem in marketing. Further, strategic recommendations for implementing company-based ethical AI marketing practices. The further strong guidelines provide enhancement transparency to consumers and investment in research to remove biases from algorithms. It also underscores principal scopes of further research and development, focusing on the need for new-age solutions to guide the ethical quagmires that AI-led marketing throws up. We suggest that integration should be done proactively and collaboratively. Thus, if attention is paid more to ethics and multidisciplinary dialogue, only such hyperbolic promises can be realized by AI-driven marketing. There are such promises through which AI-driven marketing will meet business goals and be huge at societies' values.

The rising demand for online learning and the high academic reliance on artificial intelligence (AI) have led to the need for the development of an automated system that detects and mitigates instances of student cheating, especially in large settings, to protect the academic process. The dramatic shift towards online learning, especially after COVID-19, has urged the development of such systems to ensure exam integrity. This research aims to develop an advanced Neural Network-CNN framework to detect and predict cheating in online and large-scale lab assessments. The proposed framework aimed to detect cheating through specific measures and patterns depending on students’ actions or responses, such as eye blinking, attention, lip movement, and time-automated data series. This research employed various data inputs through a complex neural network architecture during assessments, and the framework uses a multi-modal approach that analyzes all the interactions of the students and their patterns. The results show that the AI framework reduces up to 41.2% of cheating among n students and can predict cheating up to 35.3% among 100 students in one exam.