Incorporating generative artificial intelligence (GAI) in education has become crucial in contemporary educational environments. This research article thoroughly investigates the ramifications of implementing GAI in the higher education context of Saudi Arabia, employing a blend of quantitative and qualitative research approaches. Survey-based quantitative data reveals a noteworthy correlation between educators\' awareness of GAI and the frequency of its application. Notably, around half of the surveyed educators are at stages characterized by understanding and familiarity with GAI integration, indicating a tangible readiness for its adoption. Moreover, the study\'s quantitative findings underscore the perceived value and ease associated with integrating GAI, thus reinforcing the assumption that educators are motivated and inclined to integrate GAI tools like ChatGPT into their teaching methodologies. In addition to the quantitative analysis, qualitative insights from in-depth interviews with educators unveil a rich tapestry of perspectives. The qualitative data emphasizes GAI\'s role as a catalyst for collaborative learning, contributing to professional development, and fostering innovative teaching practices.

Emergency remote teaching is a temporary change in the way education occurs, whereby an educational system unexpectedly becomes entirely remote. This article analyzes the motivation of students undertaking a university course over one semester of emergency remote teaching in the context of the COVID-19 pandemic. University students undertaking a programming course were surveyed three times during one semester, about motivation and COVID concern. This work explores which student motivation profiles existed, how motivation evolved, and whether concern about the pandemic was a factor affecting motivation throughout the course. The most adaptive profile was highly motivated, more prepared and less frustrated by the conditions of the course. However, this cluster experienced the highest levels of COVID-19 concern. The least adaptive cluster behaved as a mirror image of the most adaptive cluster. Clear differences were found between the clusters that showed the most and least concern about COVID-19.

Facial images are used for kinship verification. Traditional convolutional neural networks and transfer learning-based approaches are presently used for kinship identification. The transfer-learning approach is useful in many fields. However, it does not perform well in the identification of humans\' kinship because transfer-learning models are trained on a different type of data that is significantly different as compared to human face image data, a technique which may be able for kinship identification by comparing images of parents and their children with transformed age instead of comparing their actual images is required. In this article, a technique for kinship identification using a Siamese neural network and age transformation algorithm is proposed. The results are satisfactory as an overall accuracy of 76.38% has been achieved. Further work can be carried out to improve the accuracy by improving the Life Span Age Transformation (LAT) algorithm for kinship identification using facial images.

Changes due to technological development in the workplace are putting pressure on academia to keep pace with the changing nature of work. Due to the growing need for cybersecurity professionals, universities improve their cybersecurity programs to develop qualified cybersecurity competencies. The purpose of this study is to validate the cybersecurity knowledge, skills, and abilities (KSAs) competencies of cybersecurity degree programs using a fuzzy linguistic group decision-making method. This study shows that cybersecurity knowledge is essential, along with technical skills and human abilities for cybersecurity professionals.

BACKGROUND: Insufficient educational resources on sports dentistry result in varying lecture implementations across dental schools, which cause knowledge gaps amongst students. Thus, a new interactive sports dentistry-related computer-assisted learning (CAL) module was created to facilitate dental school undergraduate students\' education. This study compared the CAL module\'s learning effectiveness with conventional video lectures (VL) and assessed its effectiveness when used over several years at a university, and examined its validation in different university contexts, and students\' perceptions.
METHODS: Participants were 305 fifth-year students. Students from a university in Tokyo participated for 3 years and those from a university in Saitama for 1 year. In each year, the students were divided into two groups-CAL and VL. They studied their assigned modules in 20-min lessons. A written test was administered to determine their knowledge acquisition levels, along with a questionnaire.
RESULTS: Two hundred sixty-two consenting participants were included in the statistical analysis. The CAL groups\' test scores at both schools were significantly higher than the VL groups\' every year (p < .001). Furthermore, test results from all years revealed no gender differences or repetition of the school years at either school. Most students at both universities evaluated the CAL module as excellent.
CONCLUSIONS: The interactive CAL module generated consistently strong results over multiple years, during which it was used by a diverse group of students at two universities. The students highly rated the module\'s learning process as well as its contents.