AUAR: AI-driven user-centric Experience in Augmented Reality for Content Creation Using Machine Learning and Geospatial Data

Advancements in AI are enhancing augmented reality (AR), leading to a new generation of systems that revolutionize personalized user experiences. This paper presents AUAR, an innovative AI-Driven User-Centric AR experience. This system leverages generative AI, machine learning, and geospatial analysis, and multi-criteria decision-making (MCDM) to model the context related to users and their environments, facilitating the generation of personalized virtual content. Implemented in a park as a case study, AUAR offers two main features: the first provides an emotion-aware AR experience, which also features haptic interactions, by creating and rendering content related to a Christmas tree. The second feature, adaptive AR services, adjusts content dynamically using Level of Detail (LOD) principles for park elements, such as walking trajectories, derived from geospatial and user-generated data while incorporating MCDM with MAIRCA analysis to enhance user interactions and decision-making. The efficacy of our system is rigorously evaluated through user feedback and statistical analysis. This research aims to create a roadmap for AI-Driven User-Centric AR experiences, focusing on optimizing system performance and enhancing user satisfaction.