Integrating artificial intelligence (AI) with immersive analytics (IA) represents a promising means of leveraging advanced computational techniques to enhance data visualization and analysis. This study examines the state-of-the-art of AI-IA integration by addressing three key research issues: the significant application domains, the AI techniques used and their combinations, and current challenges and future directions. Results of reviewing 43 relevant studies reveal that AI-IA integration is still in its early stages, as existing research has mainly focused on a limited range of data types and application scenarios. By analyzing the application domains, this systematic literature review supports previous findings of important applications in the fields of education, manufacturing, and healthcare. At the same time, it identifies emerging applications that have progressed from XR and AI domains to AI-IA integration, such as sports events, assistive systems, urban planning, and disaster management. We contribute to extending established visual analytics (VA) pipelines into XR environments with integrated AI techniques. AI techniques are identified as contributing in five ways to this IA pipeline. Our contribution also includes identifying four key challenges and seven opportunities for future exploration. The review concludes that combining AI and IA holds the potential to create innovative applications using advanced AI and immersive visualization techniques. We present an overview of these applications and address key issues for future development. 

Generative artificial intelligence (GenAI) models have advanced various applications with their ability to generate diverse forms of information, including text, images, audio, video, and 3D models. In visual computing, their primary applications have focused on creating graphic content and enabling data visualization on traditional desktop interfaces, which help automate visual analytics (VA) processes. With the rise of affordable immersive technologies, such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), immersive analytics (IA) has been an emerging field offering unique opportunities for deeper engagement and understanding of complex data in immersive environments (IEs). However, IA system development remains resource-intensive and requires significant expertise, while integrating GenAI capabilities into IA is still under early exploration. Therefore, based on an analysis of recent publications in these fields, this position paper investigates how GenAI can support future IA systems for more effective data exploration with immersive experiences. Specifically, we discuss potential directions and key issues concerning future GenAI-supported IA applications. 

Nonlinear dimensionality reduction (NLDR) is widely used to project high-dimensional data into low-dimensional embeddings for visualization and analysis. However, these embeddings are often difficult to interpret, particularly for non-expert users. Existing studies have mainly examined how dimensionality reduction results can be interpreted through conventional visualizations, while supporting NLDR understanding through immersive visualizations has received far less attention. In this paper, we present an immersive visualization system that features three immersive visual cues for interpreting NLDR embeddings: a feature attribution panel, local reliability highlighting, and neighbor relationship visualization. The system was implemented as a standalone application on a mobile VR device and evaluated through a within-subject user study with 19 participants. Based on their task performance and subjective questionnaire responses, the results show that the proposed visual cues support selected NLDR analysis tasks and improve perceived confidence in solving tasks, although the effects were not equally strong across all tasks. Overall, this work demonstrates the potential of mobile VR as a practical setting for human-centered, interpretation-oriented immersive analytics of NLDR results.

Multivariate network (MVN) analysis can benefit from immersive, embodied visualization spaces, while immersive interactions should be properly designed to effectively support analysis. We present a design-oriented study on the interaction design of immersive MVN analysis. We introduce three interaction design decisions and instantiate them in an immersive analytics (IA) system for a citation network analysis. We collected formative feedback from an observational pilot study with the system prototype and report the observations of nine participants on their analytical behavior, interaction strategies, and challenging moments. This work provides preliminary feedback and practical design considerations for interaction design in IA for MVNs.