## 5 Conclusions  
In this study, we designed and implemented an XR-based serious game to enhance viticulture training, integrating AI-driven digital twins for real-time feedback and simulated farming scenarios. To this end, we surveyed viticulture professionals to gauge their receptivity to immersive technologies and their practical needs. A total of 26 participants completed the survey, reporting an average willingness to adopt VR of 4.48, a moderate climate change concern of 4.16, and a high interest in digital solutions for pest monitoring and water management (4.85). These data points shaped the final architecture, confirming that blending immersive XR, AI, and DTs can meet both educational objectives and stakeholder expectations, thereby supporting more sustainable, scalable learning in viticulture and other woody crops.  
The findings confirm all three hypotheses for XR-based agriculture education. (H1) is validated by the successful design of an AI-driven virtual educator offering immersive, real-time feedback on viticulture practices. (H2) is supported by survey data showing an average VR-adoption willingness of 4.48, suggesting strong receptivity to XR-based learning. Finally, (H3) is fulfilled by integrating stakeholder feedback (participants indicated moderate climate change concern (4.16) and high interest in precision technologies (4.85)), thus aligning the platform with real-world needs.  
By designing a virtual educator system that integrates real-time data, predictive models, and immersive learning, we addressed the limitations of traditional training methods that often demand extensive time, resources, and on-site expertise. The integration of immersive technologies and AI in viticulture education fosters sustainable, efficient farming by simulating resource-intensive scenarios without actual water, fertilizers, or pesticides, reducing environmental impact and allowing the exploration of eco-friendly methods. These DT- and AI-driven simulations enhance soil health, water management, and pest control strategies. Moreover, incorporating such innovations into training programs immerses future farmers in AI-driven experiences, equipping them with the knowledge required to address modern agricultural challenges. Ultimately, this approach builds a technologically adept workforce, ensuring a resilient future for woody crops and viticulture in the face of climate change and food security needs.  
Therefore, this work stands out from previous research by combining DTs, AI-driven personal assistants, and XR technologies into a cohesive framework for agricultural education. Unlike earlier studies that focused solely on one aspect of these technologies, our approach integrates them to create a comprehensive educational tool that enhances both practical and theoretical learning. Future work will focus on refining these technologies and expanding their application to other areas of agriculture, further improving the sustainability and efficiency of farming practices worldwide.  
Conceptualization, O.S., M.A.-S. and S.V.; methodology, O.S. and M.A.-S.; software, O.S. and M.A.-S.; validation, O.S., M.A.-S. and S.V.; formal analysis, O.S., M.A.-S. and S.V.; investigation, O.S. and M.A.-S.; resources, O.S., M.A.-S. and S.V.; data curation, O.S., M.A.-S. and S.V.; writing--original draft preparation, O.S., M.A.-S. and S.V.; writing--review and editing, O.S., M.A.-S. and S.V.; visualization, O.S., M.A.-S. and S.V.; supervision, S.V. All authors have read and agreed to the published version of the manuscript.