## 5 Implementation and User Scenario
### Technical Validation and Experimental Results
#### 5.4.5 Results of UI/UX Performance  
* **Cross-Platform Performance Evaluation**  
The system's performance was evaluated across five different UI platforms (web, mobile, VR, Smartwatch UI, and Automotive Interface) to ensure consistency in interaction and experiences. Table 6 presents the performance evaluation results of AI-MDD-UX across these platforms. AI-MDD-UX achieved a user experience score of 9.4 with an average user satisfaction of 94.7% in the VR platform, highlighting its strong engagement in immersive environments. Compared to the web platform (user satisfaction 91.2%, user experience of 9.0), AI-MDD-UX showed a slightly reduced user experience, with an unchanged UI layout. Smartwatch UI results show relatively lower click-through rate (CTR: 8.9%) and conversion rate (32.1%), which can be attributed to the limited screen real estate and interaction time. Despite this, a user experience score of 8.1 and user satisfaction of 85.5% indicate that AI-MDD-UX successfully maintained functionality and clarity in a compact interface. The system prioritized glanceable content, adaptive button resizing, and gesture-aware navigation to enhance interactions. In the Automotive Interface, the system achieved a user satisfaction rate of 90.3% and a user experience score of 8.8, reflecting the effectiveness of context-aware UI adaptations. The system dynamically minimized distraction by prioritizing essential content, adjusting contrast and layout based on lighting conditions, and suppressing non-critical elements during driving. Despite safety constraints, it maintained a solid CTR (11.1%) and conversion rate (37.8%) through voice-activated menus and driver-profile-based personalization. Figure 12: Execution speed over sessions. Overall, the results indicate that AI-MDD-UX significantly improved conversation rates and session duration. The improvements stem from behavior-driven UI refinement, adaptive rules, and reinforcement learning (RL)-based optimizations that dynamically adjusted the interface in response to user interactions. Smartwatch and automotive results reinforce the system's potential in minimalist and safety-critical interfaces, although further tuning is required to maximize interactivity without overwhelming the user. * **Evaluation with Simulated User Personas** To comprehensively evaluate the system's adaptability, we conducted testing using simulated user personas representing distinct interaction behavior: (1) Power User, characterized by frequent engagement, preference for shortcuts and rapid navigation, (2) Cautious Browser, exhibiting thorough exploration before taking action, (3) First-Time User, lacking interaction history and requiring additional guidance and (4) Accessibility-Focused User which relies on larger text. As shown in Table 7, our performance evaluation demonstrated AI-MDD-UX's robust personalization capabilities. The system showed particular effectiveness for power users, who benefited from efficiency-optimized layouts and interaction shortcuts. While first-time and accessibility-focused users exhibited slightly longer task completion times due to their need for adaptive guidance and specialized interface elements, satisfaction levels remained consistently high across all groups. Notably, all personas rated the adaptive UI above 8.7, confirming the system's capacity to accommodate diverse needs and usage habits. * **Evaluation with Various Business Domains** To further demonstrate the broad applicability of the AI-MDD-UX framework, we tested it in various business domains (healthcare, education, finance, and retail), focusing on user satisfaction and user experience. As shown in Figure 13, in the healthcare domain, physics had a higher user experience score (9.3) compared to patients (8.4), due to the system's greater utility for healthcare professionals. In education, advanced learners scored higher (9.5) than new learners (8.7), reflecting greater familiarity and effective use of the system.