The present study explored the determinants influencing students’ intentions towards utilizing digital learning technologies (DLTs). It proposes a holistic view model for students’ utilization of digital learning technologies by integrating social support theory and the “Technology Acceptance Model” (TAM). Data were gathered from 262 students of the University of Ha’il through utilizing a questionnaire. Two steps in SEM were applied for data analysis: CFA was employed to develop the model measurement, and SEM was conducted to analyze the relationships between constructs. The results revealed that “Educational Support” (EDS) significantly affected students’ “behavior intention” (BI) to utilize digital learning technologies through perceived usefulness (PU) (EDS→PU: β = 0.338, p < 0.05). Furthermore, “Emotional support” (EMS) significantly affected behavioral intention through both “perceived ease of use” (PEU) and PU (EMS→PEU: β = 0.635, p < 0.05; EMS→PU: β = 0.310, p < 0.05). Surprisingly, students’ “perceived ease of use” (PEU) did not affect digital learning technologies’ “perceived usefulness” (PU) (PEU→PU: β = 0.195, p > 0.05). The present study contributes to a refined understanding of how educational and emotional support influence students’ receptivity towards and engagement with learning technologies. Thus, institutions and universities should enhance their students’ educational and emotional support to facilitate the successful utilization of digital learning technologies. Furthermore, the study revealed that PU and PEU affected students’ intentions to utilize DLTs. Thus, PU and PEU can enhance the utilization of digital learning technologies. This study provides practical implications that educational institutions and universities could apply to achieve successful usage of these digital learning technologies.

The current study aims at assessing the factors which could affect students' use of ChatGPT. The study proposed a theoretical model that included five factors. Data were collected from 136 students using a questionnaire. The data were analyzed using two steps: CFA for measuring the model and SEM for analyzing the relationships and testing hypothesis. The findings revealed that both performance expectancy and facilitating conditions significantly influenced students' intentions to use ChatGPT. Contrary to expectations, both social influence and effort expectancy had insignificant effects. By elucidating the core factors influencing the utilization of ChatGPT, this study can provide valuable insights for policymakers. Furthermore, this study contributes to the existing literature and lays the foundation for future research seeking a deeper understanding of the factors influencing the use of other AI technologies in teaching and learning.

Virtual classrooms have recently gained significant consideration in educational institutes and universities due to their potential to encourage and support students’ learning activities. Although recent research has focused extensively on online learning, virtual classrooms and the factors affecting their continuous use have garnered little attention, especially in Arab Gulf countries such as Saudi Arabia. Thus, this study integrates the expectation confirmation model and the information systems success model to assess the factors affecting students’ continuous intention to utilise virtual classrooms in higher education. We examined the effects of information quality, service quality, system quality, confirmation, perceived usefulness, and satisfaction on the continuous intention to utilise virtual classrooms. Data were collected from 441 students and analysed using structural equation modelling “SEM”. SEM is a powerful multivariate approach used increasingly in empirical investigation for evaluating and testing casual relationships. The results revealed that the proposed model demonstrated high explanatory power in explaining students’ continuous intention to utilise virtual classrooms (R² = 0. 86). Additionally, information quality had a significant effect on confirmation and an insignificant effect on perceived usefulness. System quality affected perceived usefulness and confirmation. Contrary to our expectations, service quality had a significant negative effect on perceived usefulness and confirmation. Additionally, perceived usefulness and confirmation affected students’ satisfaction with using virtual classrooms, and satisfaction affected students’ continuous intention to utilise virtual classrooms. This study contributes to the literature by offering a holistic integrated model that increases the understanding of the factors influencing students’ continuous intention to utilise virtual classrooms, hence aiding in increasing their utilisation. Furthermore, it provides practical implications for enhancing students’ continuous intention to utilise virtual classrooms. Virtual classroom developers must focus on improving the system quality of virtual classrooms. According to our results, higher system quality led the students to perceive virtual classrooms as useful and confirmed their favourable experiences with virtual classrooms. Additionally, providing students with high information quality in virtual classrooms would enhance their confirmation experiences, leading to the continuous intention to utilise virtual classrooms.

Using the Community of Inquiry (CoI) framework, this study investigates the effects of teaching presence, social presence, and cognitive presence on student engagement in online courses. Most existing research examines engagement in general, but this study assesses engagement by its three fundamental aspects of behaviour, emotion, and cognition due to the heightened need for understanding student engagement in post-pandemic online learning. Structural equation modelling (SEM) was used to analyse survey data from 220 university students taking online courses. The results revealed that both social presence and cognitive presence have a significant effect on student engagement in online learning environments and are key influences in online learning. Interestingly, teaching presence did not directly impact student engagement, which implies that instructor facilitation may not be sufficient to increase active participation. These results emphasise the critical importance of supporting social interactions that are meaningful and deep cognitive engagement for maximising experiences for learning in virtual environments. This study provides insights for educators and course designers who want to increase engagement and enhance learning achievement in online education. In practice, developing strategies that enhance social and cognitive presence, including promoting peer collaboration and incorporating thought-provoking content, should be emphasised.

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.

Although the use of ChatGPT in the educational field has spread rapidly in universities, there is little information on factors affecting students’ intentions toward using ChatGPT to support their learning. This study bridges the gap by integrating the extended Technology Acceptance Model (TAM) (including awareness and trust) with Expectation Confirmation Model (ECM) constructs, namely confirmation and satisfaction. This proposed model provides an in-depth understanding of students’ willingness to use ChatGPT. Data was collected from 322 university students and analyzed using a second-generation analysis technique, Structural Equation Modeling (SEM) using AMOS. The results revealed that awareness significantly positively affected students’ perceived usefulness (PU) and perceived ease of use (PEU). Furthermore, trust had a significant positive effect on PU, but an insignificant effect on PEU. In addition, PU, PEU, and confirmation significantly positively affected students’ satisfaction, affecting their behavioral intention toward using ChatGPT for their learning. Furthermore, PU and PEU significantly positively affected students’ behavior intention toward using ChatGPT. This study offers recommendations to developers of ChatGPT, policymakers, and educational institutes by understanding the influential factors on students’ willingness to use ChatGPT. This study assists ChatGPT developers and designers by offering insight regarding designing and improving the user’s secure and friendly system, which may enhance the use of ChatGPT among students.