Currently, new technologies have enabled the design of smart applications that are used as decision-making tools in the problems of daily life. The key issue in designing such an application is the increasing level of user interaction. Mixed reality (MR) is an emerging technology that deals with maximum user interaction in the real world compared to other similar technologies. Developing an MR application is complicated, and depends on the different components that have been addressed in previous literature. In addition to the extraction of such components, a comprehensive study that presents a generic framework comprising all components required to develop MR applications needs to be performed. This review studies intensive research to obtain a comprehensive framework for MR applications. The suggested framework comprises five layers: the first layer considers system components; the second and third layers focus on architectural issues for component integration; the fourth layer is the application layer that executes the architecture; and the fifth layer is the user interface layer that enables user interaction. The merits of this study are as follows: this review can act as a proper resource for MR basic concepts, and it introduces MR development steps and analytical models, a simulation toolkit, system types, and architecture types, in addition to practical issues for stakeholders such as considering MR different domains.

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.