The emergence of generative artificial intelligence (Gen AI) in education offers both opportunities and challenges, particularly in the context of student assessment. This study examines faculty members’ motivations to redesign assessments for their courses in the Gen AI era and introduces a framework for this purpose. A qualitative methodology was employed, gathering data through semi-structured interviews and focus groups, along with examples of redesigned assessments. Sixty-one faculty members participated in the study, and the data were analyzed using both deductive and inductive thematic approaches. Key motivations for redesigning assessments included maintaining academic integrity, preparing learners for future careers, adapting to technological advancements, and aligning with institutional policies. However, the study also highlighted significant challenges, such as the need for professional development and addressing equity and accessibility concerns. The findings identified various innovative assessment approaches tailored to the requirements of the Gen AI era. Based on these insights, the study developed a conceptual framework titled “Against, Avoid, Adopt, and Explore”. Future research is needed to validate this framework and further refine its application in educational contexts.

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.