Artificial intelligence is user-friendly and incorporates a useful number of characteristics that are common across the various services that are provided. By enhancing inventive engagement, artificial intelligence (AI) applications enable a more participatory setting in government agencies. The objective of this research is to find out how the UAE consumers feel about using AI in educational settings. Included in the framework are the characteristics of acceptance, which are: perceived compatibility, trialability, relative advantage, ease of doing business, and technology export. 466 questionnaires from various universities have been gathered. The research model was examined using machine learning algorithms (ML) and partial least squares-structural equation modeling (PLS-SEM), which centered on the student's questionnaire responses. The IPMA is also used in this research to evaluate performance and importance of the variables. The theoretical framework of the research links the qualities of the individual variables and those of the technology which makes it new. The findings indicate that the diffusion theory factors outperform the other two factors of ease of doing business and technology export. It ought to be mentioned that when it pertains to the estimated value of the dependent factor, the J48 classifier largely outperformed other classifiers. This study’s findings can guide educational institutions in the UAE to recognize the importance of each acceptance factor in the successful integration of AI technologies. Institutions can use these insights to tailor their strategies, enhancing AI adoption among students and faculty alike. Specifically, the results suggest prioritizing factors from diffusion theory in educational AI implementations, ensuring these technologies are perceived as advantageous and compatible with existing practices. Furthermore, the superiority of the J48 classifier suggests that similar analytical techniques could be employed by educational institutions to continually assess and improve their AI initiatives. The dominance of diffusion theory factors invites further exploration into how these elements specifically influence AI acceptance in other sectors or regions. Additionally, the comparative underperformance of ease of doing business and technology export as factors suggests a need for deeper investigation into how these dimensions can be better leveraged in the context of AI in education. Future research could also explore longitudinal studies to assess the sustainability of AI acceptance over time and experiment with integrating new machine learning algorithms to compare their predictive power against the J48 classifier in different educational settings.