The integration of artificial intelligence (AI) into educational settings has the potential to transform learning experiences; however, its adoption among students remains impacted by various factors. The current study assesses the determinant factors that shape students’ willingness to adopt AI applications. A quantitative approach was applied, and data were collected from 211 undergraduate students at the University of Ha’il. The data were analysed via structural equation modelling (SEM) with the AMOS software. The results revealed that readiness, interactivity, and ethical awareness significantly affect students’ adoption of AI applications, while trust and performance did not have a significant effect. These findings suggest that fostering AI adoption among students requires a holistic approach that addresses students’ technical preparedness and considers trust-building and ethical educational usage. The implications of this study are significant for educational institutions such as universities, as well as for policy makers, educators and developers aiming at integrating the AI applications effectively into students’ learning and curriculum. University administrators could benefit by developing the necessary infrastructure and training, while educators could leverage these insights in designing AI-supported learning environments. Students would benefit from these improved academic experiences that are tailored to their needs. This research contributes to the growing discourse on AI in education and offers a holistic conceptual framework to maximize its potential.

The current study investigates the factors affecting students' behavioral intentions to utilize "artificial intelligence" (AI) applications in learning by extending the "unified theory of acceptance and use of technology" (UTAUT) model with the inclusion of cognitive flexibility. Data were collected from 315 undergraduate students and analyzed employing a two-step approach involving "structural equation modeling" (SEM) with AMOS. The measurement model was first run to assess and confirm the reliability and validity of the variables, followed by the structural model to test the hypothesized relationships. The results revealed that "performance expectancy (PE), effort expectancy (EE), and social influence" (SI) exerted significant positive effects on students' behavioral intentions to use AI applications, while "facilitating conditions" (FC) exhibited no significant impact. Additionally, "cognitive flexibility" (CF) did not directly influence behavioral intention but exerted a significant indirect effect through its influence on PE, EE, and SI. These findings underscore the significance of students' cognitive adaptability in shaping their perceptions of technological usefulness and ease of use and highlight the value of integrating psychological variables into technology acceptance models in educational contexts. Implications for educational technology implementation and future research directions are discussed.

During the past two decades, many universities around the world have deployed various types of learning management systems (LMSs) to manage the learning and teaching process electronically. The rapid spread of the coronavirus disease at the start of the COVID-19 pandemic quickened educational systems’ shifting from face-to-face to online instruction. Blackboard is the most used LMS during this transition in Saudi Arabian universities, institutions, and higher education institutions. Although previous research has widely investigated students’ adoption of Blackboard, few studies have examined the factors affecting their readiness toward e-learning. The present study used the model of Student Online Learning Readiness (SOLR) as the primary framework for examining the factors that may affect students’ readiness toward using Blackboard. A survey was delivered to undergraduate and postgraduate students at the University of Ha’il to collect data, and a total of 196 responses were received. A two-step process in AMOS was applied to analyse the data. The study findings showed that technical, social, and communication competencies had a significant positive influence on the readiness of students toward using Blackboard LMS. The study outcomes will provide educators, designers, decision-makers, and practitioners with a deeper understanding of the factors that could influence students’ readiness toward using Blackboard and, thus, enhance the successful usage of the system.

The metaverse, an interactive and immersive 3D virtual environment, has recently become popular and is widely used in several fields, including education. However, the successful use of metaverse relies on the extent to which users intend to adopt and use it. Close examination of this critical issue reveals a lack of research that examines the effects of certain factors on users’ intentions toward using metaverses. Thus, this study extends the technology acceptance model by integrating two constructs—perceived system quality and students’ personal innovativeness. Using a survey to collect data, 164 responses were received from students at the University of Ha’il in Saudi Arabia. Two steps in structural equation modelling (SEM) using the AMOS software were applied to analyse the data and test the research hypotheses. The results revealed that perceived system quality had a significant effect on students’ intentions to use metaverses through perceived ease of use. Furthermore, personal innovativeness had a significant effect on students’ intentions through the perceived usefulness of the metaverse. In addition, perceived usefulness affected students’ intentions to use a metaverse. Surprisingly, perceived ease of use had an insignificant effect on students’ intentions to use the metaverse. Although the proposed model and its findings contribute to the technology acceptance model (TAM) literature, the study’s practical value is significant because it can help educational policymakers and authorities to understand the effect of each factor and plan future strategies. Additionally, the findings of this study can assist practitioners, designers, and developers in designing and promoting the utilisation of metaverses.