Virtual reality (VR) technologies have become powerful tools for delivering interactive learning experiences, offering controlled and adaptive environments for skill development. This study introduces an AI-enhanced standalone VR training framework designed for adolescents with autism spectrum disorder (ASD), integrating two interactive environments—a restaurant and a classroom—to support both life skills and educational competencies. The system was developed using Unity and Blender and delivered through Oculus Quest 3 standalone head-mounted displays with a dual-interface monitoring system for therapists and parents. A structured four-phase protocol (orientation, environment-specific training, integrated practice, and assessment) guided 15 participants (11 males, 4 females), aged 10–13 years, through 12 standardized sessions, producing 180 session-level records. Quantitative data included task completion time, error frequency, number of attempts, and interaction patterns, while qualitative feedback was collected from therapists and parents. Statistical analyses (paired t-tests, repeated measures ANOVA) revealed significant phase-related changes in completion time, error frequency, and task attempts, while success rates remained stable. An AI component using a Decision Tree classifier achieved 70.4% accuracy in predicting task outcomes, providing preliminary evidence for the role of machine learning in adaptive feedback and personalized interventions. Findings suggest that the proposed standalone VR framework enhances engagement and skill development among adolescents with ASD while offering valuable analytics for educators and therapists. The integration of VR, AI, and dual-environment design underscores the potential of immersive technologies to support scalable, adaptive, and data-driven interventions in special education.

Mobile learning application now is considered a powerful application for learning and was adopted in universities in the period of Covid-19. After Covid-19 pandemic, university students have been allowed to use mobile learning systems, it is needed to ensure students’ intention to continuously use mobile learning for their learning activities or not. Thus, the purpose of this paper is to understand the main determinants that encourage the continuous use of mobile learning. To achieve that, we used the UTAUT-2 model to predict the main determinants of mobile learning acceptance. In our study, a quantitative technique was employed to collect the data. A hybrid approach SEM-ANN was applied to validate the research model. The findings indicated that performance expectancy and effort expectancy had a strong effect on students' attitudes towards mobile learning platforms. In addition, the results showed that performance expectancy and effort expectancy have a significant impact on students' continuous intention to use mobile learning platforms after Covid-19. In addition, hedonic motivation and habit had a positive effect on both students' attitudes and continuous intention to use mobile learning platforms. Moreover, Social influence factor and facilitating conditions had a significant effect on students' continuous intention to use mobile learning platforms after Covid-19.

This paper focuses on the impact of fear emotion upon technology adoption by educators and students during Covid-19 pandemic. Mobile learning (m-learning) has been applied as the educational social platform within higher education institutes, public as well as private. The research hypotheses were associated with the Covid-19 influence on m-learning adoption with the rise of the coronavirus increasing types of fear. Such fears include fear caused by the education failure, family lockdown, and loss of social relationships. Teachers and students are mostly fearful of these aspects of the situation. An integrated model was established within the research, using theoretical models; the Planned Behavior theory, the Technology Acceptance Model, and the Expectation-Confirmation Model. The proposed integrated model (using PLS-SEM software) was analyzed using an online survey data, with 420 respondents from Zayed University, UAE. The findings indicated that attitude was the best predictor for using the m-learning system, followed by continuous intention, expectation confirmation, perceived usefulness, ease-of-use, perceived fear, behavioral control, and satisfaction. According to the research, during the coronavirus pandemic, if the m-learning system is adopted for educational reasons, the learning and teaching outcome proves quite promising. Yet there is a fear of the family being stressed, or of loss of friends, and also a fear of the results of future schooling. It is therefore necessary to assess the students efficiently during this pandemic so that the situation can be managed emotionally.