Advanced AI Models for UI Optimization to Improve Digital User Experience

The digital era demands user interfaces (UI) and user experiences (UX) that are adaptive, intuitive, and personalized. Traditional manual UI design approaches often fail to meet dynamic user expectations, resulting in rigid and inefficient interactions. This study aims to investigate the impact of advanced artificial intelligence (AI) models on optimizing digital user interfaces to enhance both usability and overall user experience. A mixed-methods explanatory sequential design was employed, combining quantitative evaluation of AI-driven UI performance with qualitative assessment of user perceptions. Deep neural networks, reinforcement learning, and natural language processing were implemented in experimental interface scenarios to measure task completion rates, error reduction, navigation efficiency, and user engagement. The results demonstrated significant improvements in performance metrics, including a 14% reduction in task completion time, a 35% increase in click efficiency, and a notable rise in user satisfaction and trust. Qualitative findings indicated enhanced emotional comfort, reduced cognitive load, and positive perceptions of adaptive personalization. The study concludes that integrating advanced AI in UI design substantially improves both functional and experiential outcomes. These findings provide theoretical contributions to human-centered AI frameworks and practical guidance for designing adaptive, transparent, and contextually intelligent digital interfaces.