Background: Artificial intelligence (AI) has many applications in various aspects of our daily life, including health, criminal, education, civil, business, and liability law. One aspect of AI that has gained significant attention is natural language processing (NLP), which refers to the ability of computers to understand and generate human language. Objective: This study aims to examine the potential for, and concerns of, using AI in scientific research. For this purpose, high-impact research articles were generated by analyzing the quality of reports generated by ChatGPT and assessing the application’s impact on the research framework, data analysis, and the literature review. The study also explored concerns around ownership and the integrity of research when using AI-generated text. Methods: A total of 4 articles were generated using ChatGPT, and thereafter evaluated by 23 reviewers. The researchers developed an evaluation form to assess the quality of the articles generated. Additionally, 50 abstracts were generated using ChatGPT and their quality was evaluated. The data were subjected to ANOVA and thematic analysis to analyze the qualitative data provided by the reviewers. Results: When using detailed prompts and providing the context of the study, ChatGPT would generate high-quality research that could be published in high-impact journals. However, ChatGPT had a minor impact on developing the research framework and data analysis. The primary area needing improvement was the development of the literature review. Moreover, reviewers expressed concerns around ownership and the integrity of the research when using AI-generated text. Nonetheless, ChatGPT has a strong potential to increase human productivity in research and can be used in academic writing. Conclusions: AI-generated text has the potential to improve the quality of high-impact research articles. The findings of this study suggest that decision makers and researchers should focus more on the methodology part of the research, which includes research design, developing research tools, and analyzing data in depth, to draw strong theoretical and practical implications, thereby establishing a revolution in scientific research in the era of AI. The practical implications of this study can be used in different fields such as medical education to deliver materials to develop the basic competencies for both medicine students and faculty members.

This study examines the factors that may impact the adoption of generative artificial intelligence (Gen AI) tools for students’ assessment in tertiary education from the perspective of early-adopter instructors in the Middle East. It utilized a self-administered online survey and the Unified Theory of Acceptance and Use of Technology (UTAUT) model to collect data from 358 faculty members from different countries in the Middle East. The Smart PLS software 4 was used to analyze the data. The findings of this study revealed that educators developed new strategies to integrate Gen AI into assessment and used a systematic approach to develop assignments. Moreover, the study demonstrated the importance of developing institutional policies for the integration of Gen AI in education, as a driver factor influencing the use of Gen AI in assessments. Additionally, the research identified significant factors, namely performance expectancy, effort expectancy, social influences, and hedonic motivation, shaping educators’ behavioral intentions and actual use of Gen AI tools to assess students’ performance. The findings reveal both the potential advantages of Gen AI, namely enhanced student engagement and reduced instructor workloads, and challenges, including concerns over academic integrity and the possible negative impact on students’ writing and thinking skills. This study emphasizes the significance of targeted professional development and ethical criteria for the proper integration of Gen AI in educational assessment.

The acceptance and adoption of emerging technologies are crucial for their effective integration. This study examines the factors influencing educators’ acceptance of Generative AI (Gen AI) tools in higher education, guided by the UTAUT model. It also develops a structural model to explore the relationships between UTAUT constructs and behavioral intention (BI) to use Gen AI. Using a quantitative approach, the study collected data through a self‐administered online survey based on prior research findings. The survey gathered responses from 307 educators across various Arab countries who are early adopters of Gen AI in teaching. PLS‐SEM was used to analyze the data. Findings indicate that UTAUT constructs significantly and positively influence educators’ intention to use Gen AI. Additionally, the results highlight the complex role of gender and work experience, revealing diverse perspectives among educators from different countries. This study contributes to the literature by deepening the understanding of technology adoption factors. It also offers theoretical and practical implications for researchers and policymakers in designing strategies to integrate Gen AI into higher education in developing countries.

This study delves into online assessments, focusing on their numerous advantages, including reducing paper usage and alleviating concerns surrounding the security of traditional examinations. The purpose of this study was to develop and test a model delineating the interplay between factors influencing the continuance intention to use online assessments and the influence on individual performance. A quantitative approach using an online survey was used to collect data from 222 graduate students from three major Palestinian universities. The research instrument was developed based on the finding of previous studies. Confirmatory factor analysis was employed to construct and validate the proposed model. The findings of this study revealed that perceived usefulness has a significant influence on the intention to continue using online assessment. The proposed model indicated that there is no direct or indirect relationship between perceived ease of use and the continuance intention to use online assessment. The main limitation of this study was its reliance on a purely quantitative approach. Therefore, a sequentional mixed methods approach van be a suggestion for future research and include all the higher education institutions in the Palestinian context.

Technostress is defined as any unhealthy condition caused by dealing with modern technology in various harmful ways; examples of technostress include addiction and stress. Even though technostress has been extensively studied in the literature, little attention has been paid to investigating technostress among academics who work in exceptional circumstances, such as crises, and who might be experiencing different psychological states due to those circumstances. To fill the gap, this study aims to explore the factors influencing technostress among school teachers. The study examined technostress's level and factor graphics structure among 692 academics from different Arab countries during COVID-19. The technostress factors and their stories were explored and measured using sequential mixed methods and confirmatory and exploratory factor analysis. The study discusses various factors' direct and indirect effects on mobile technology integration in education and the theoretical and practical implications of managing technostress in online classes. A model of techno-stressors among Arab academics was found to include: schedule overload, complexity, uncertainty, uselessness, invasion, and compulsion. The direct effect of various factors on mobile technology integration in education is mainly positive, while indirect effects are more varied. The theoretical and practical implications of managing technostress in online classes include: considering the psychological and physiological impact of technostress on students' learning performance, decreasing overall satisfaction with the learning experience, and improving the overall quality of online courses. As a result of this study's findings, a new perspective is provided on how academics in particular circumstances (in this study, the occupation of Palestine) may behave and feel toward technology in teaching.