This study delves into the intricate landscape of metaverse technology adoption within higher education institutions in the UAE, investigating the multifaceted interplay of accessibility, technology adaptability, and policies and regulations. Using a cross-sectional research design, data was meticulously collected through a multistage sampling approach, combining probability and non-probability methods. A pretested questionnaire underwent rigorous evaluation, ensuring unbiased item formulation and adherence to best practices. The investigation challenges and extends the Technology Acceptance Model (TAM) by revealing unexpected findings. The absence of a significant relationship between accessibility and metaverse adoption prompts a call for an expanded TAM framework. Surprisingly, a negative correlation between technology adaptability and adoption is highlighted, emphasizing the need for a cautious assimilation approach. Moreover, the research underscores the influential role of policies and regulations in metaverse adoption, advocating for a comprehensive TAM framework that encompasses regulatory dynamics. Findings offer practical implications for stakeholders, policymakers, and institutions, emphasizing diverse adoption facets beyond accessibility. The study contributes to the discourse on metaverse adoption and advances theoretical frameworks for technology integration within educational contexts. The methodology's meticulous design underscores the study's rigor, ensuring the robustness of the insights gleaned from the investigation.

The use of data mining to predict early employment readiness of students is gaining importance due to the expansion of data production in various industries. This study aims to address the employability issue in Middle Eastern nations by utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) data mining technology. The experimental investigation used data from tracer studies conducted by three Jordanian universities, consisting of 22 parameters. Results showed that despite achieving an accuracy of 94% for the graduate dataset, ANFIS exhibited high complexity due to the large number of attributes used. The study has implications for selecting relevant variables and investigating multiple aspects. Data mining has various applications, including classification, clustering, regression, association rule development, and outlier analysis. As data production continues to expand, this study provides insights into the potential use of ANFIS in predicting early employment readiness of students in Middle Eastern nations.

The metaverse is an imaginary network of parallel universes. Using this technology might liven up dull lecture halls. By expanding synchronous communication into the "metaverse," many individuals may have meaningful conversations and exchange perspectives. This research focuses on finding out how medical students in the UAE feel about the metaverse system. The conceptual model incorporates elements from the Technology Acceptance Model (TAM), including perceived value and perceived ubiquity as adoption determinants. To test the validity of the suggested framework, a survey was developed and distributed to 369 full-time students at one of the universities in the United Arab Emirates (UAE). Machine learning (ML) and structural equation modeling using partial least squares (PLS-SEM) are used for data analysis. According to the results, the extent to which users saw value in and adoption of the metaverse system was a significant factor in whether or not they intended to participate. This study was helpful since it elucidated the relative significance of various healthcare components, allowing professionals to prioritize their efforts better.