Exploring New Horizons in Dental Education: Leveraging AI and the Metaverse for Innovative Learning Strategies
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Abstract
The COVID-19 pandemic significantly disrupted dental education, hindering hands-on exposure to cutting-edge dental practices and advanced equipment worldwide. Amidst these challenges, the emergence of Metaverse-based learning has presented an innovative solution, fulfilling the growing need for remote educational opportunities in dentistry. Traditional online learning platforms like Zoom have proven inadequate for providing a comprehensive learning experience in dentistry, prompting a shift towards more engaging, immersive educational settings. This trend is especially evident in the dental education sector in the United Arab Emirates (UAE). This research delves into dental students‘ perceptions of how Metaverse technology aids in achieving their educational objectives within the UAE. Our analysis focuses on crucial factors that influence technology adoption, particularly ‘Perceived Value’ and ‘Perceived Satisfaction’. We collected a substantial dataset of 89 responses from the College of Dental Medicine (CDM) at the University of S harjah. To rigorously examine our research model, we applied Partial Least S quares-Structural Equation Modeling (PLS -S EM) and an advanced Machine Learning (ML) technique, based on data from our student survey. Our results highlight the Metaverse's critical role in guiding technology adoption decisions, strongly driven by ‘Perceived Value’ and ‘Perceived Satisfaction’. Remarkably, the ML method demonstrated higher predictive accuracy in identifying the outcome variable compared to other analysis techniques. This research contributes to the scholarly conversation on artificial intelligence, especially its relationship with environmental sustain ability, offering valuable insights for industry stakeholders, policymakers, and AI developers. The findings lay the groundwork for de veloping AI-powered solutions aligned with user preferences and environmental co n si de rati o n s.
Publication details
- DOI
- 10.1109/compsac61105.2024.00298
- OpenAlex
- W4401880244
- Document type
- conference-paper
- Language
- EN
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