Improved Real Time User Interaction in Extended Reality Systems Using the Deployment of Adaptive Intelligent Technologies

The Human-Computer Interaction (HCI) field has seen rapid growth in various industries due to the introduction of Extended Reality (XR) environments. These environments require improved interface methods, real-time processing, low latency, and integrated User Experience (UE) servicing. This work aims to improve user interactions in real-time XR environments and introduces a new Hierarchical Adaptive System (HAS) to address these challenges. This study presents a Real-Time Adaptation Model (RTAM) for XR interfaces, which combines adaptive optimized performance, Deep Reinforcement Learning (DRL), and Fuzzy Logic (FL). The system addresses unpredictability, Dynamic Resource Allocation (DRA), and parallel processing pipelines. HAS did better than the best methods by 46.3% in terms of Faster Learning Integration (FLI), 63.2% in terms of Lower Error Rates (LER), and 37.4% in terms of Reduced Task Completion Times (RTCT) in a study with 60 users in different interactive settings. Despite maintaining low adaptation latency, the system achieves a score of 0.86 for resource utilization efficiency. The study also identified improvements in system responsiveness and overall satisfaction. The results support that HAS effectively solves RTAM issues in XR settings, laying the basis for next-generation immersive apps with more responsive and user-centered communication models.