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. 

Extended reality (XR) technologies are increasingly being used in different application areas. One such area is for healthcare, which has seen significant developments over the last few years. However, its use for healthcare education is still in its infancy. This paper presents a case study, which explores the use of virtual reality (VR) technology in the healthcare domain. In particular, an application targeting education of various conditions healthcare providers might meet in older adult care is evaluated using different subjective evaluations methodologies, with nursing students and professional healthcare staff. The overall results show promising directions and use of new technology applications in this domain, but also highlights some of the potential problems with its adoption. 

This paper presents a conceptual design for a virtual reality (VR)-based furniture arrangement system, proposed to leverage immersive technologies and generative AI (GenAI) with intuitive interaction methods. The system utilizes a head-mounted display (HMD) to provide an immersive and intuitive user experience (UX) with multimodal interaction methods through hand and eye-tracking controls. Additionally, GenAl enables the virtual agent to engage in natural conversations with users and interpret and respond contextually, aiming to enhance personalization by understanding conversations. Users' interests and preferences are analyzed and predicted from the conversation and eye-gaze data, which provides recommendations for relevant furniture items and real-time personalized feedback. By combining these techniques, this design aims to create a seamless, interactive, intelligent, personalized virtual interior design and furniture arrangement experience within the immersive virtual environment (VE). The implementation of several key features demonstrates a proof of concept for our virtual furniture arrangement system. 

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. 

The 'Human-Centered Intelligent Realities' (HINTS) laboratory is a strategic infrastructure project aiming to support research that advances the development of immersive, user-aware, and intelligent digital environments by integrating augmented reality (AR), virtual reality (VR), extended reality (XR), artificial intelligence (AI), and machine learning (ML). By combining virtual reality and communication-computing continuums, the HINTS environment seeks to create innovative concepts, methods, and tools that empower users to engage with digital systems in novel, efficient, and effective ways. Research in the HINTS laboratory focuses on experience assessment, new digital environments and interaction techniques, visual analytics, adaptive AI, and networking. This paper presents the HINTS laboratory, ongoing activities, and opportunities and challenges for the future.

Digitaliseringen och användandet av immersiva teknologier har ökat inom många områden. Samtidigt har utvecklingen av artificiell intelligens (AI) och spelifierat lärande (gamification) också fått en betydande roll inom många applikationer. Denna rapport presenterar en kunskapsöversikt över nuvarande forskning och utveckling inom användandet av spelifierat lärande, uppslukande (immersiva) teknologier och intelligenta verkligheter för utbildning och övning i civil beredskap. Rapporten har människan i fokus för att hjälpa individer och organisationer att förbereda sig för och reagera på nödsituationer och katastrofer. Resultaten baseras mestadels på en systematisk litteraturgranskning och diskuterar hur ny teknik kan appliceras inom civil beredskap. Samtidigt identifieras utmaningar, luckor och framtida riktningar för vidare arbete inom detta växande område. 

Digitization and the use of immersive technologies have increased in many areas. At the same time, the development of artificial intelligence (AI) and gamification has also gained significant importance in various applications. This report presents a knowledge overview of current research and developments using gamified learning, immersive technologies, and intelligent realities for training and exercise in civil preparedness. The report focuses on helping individuals and organizations prepare for and respond to emergencies and disasters. The results are primarily based on a systematic literature review and discuss how new technologies can be applied in civil preparedness. At the same time, challenges, gaps, and future directions for further work in this growing field are identified.

In this paper, we investigate the current state and development of personalized smart immersive extended reality environments (PSI-XR). PSI-XR has gained increasing traction across various fields such as education, entertainment, and healthcare, offering customized immersive experiences that address users’ personalized needs. This study performs a systematic literature review by collecting and analyzing related journal and conference papers in the domain. Following a comprehensive search across three databases, which yielded 1276 papers, a refined selection of 94 publications was made to conduct an in-depth analysis of cutting-edge research in the field of PSI-XR. This review focused on examining application domains, relevant technologies, and smart techniques, including artificial intelligence, with particular emphasis on advancements in personalization. The study provides insights into prospective advancements while also identifying the opportunities and challenges in this evolving field. This review is beneficial for both researchers and developers interested in exploring the state-of-the-art personalized perspective in a smart immersive extended reality environment. 

Electrodermal activity is considered one of the more reliable methodologies to assess emotional arousal, and it has been adopted as an alternative method for the analysis of player experience in video game research. In this study, we present a quantitative evaluation of self-contained electrodermal measurements, focusing on the analysis of the tonic component and its potential relationship with player experience. The analysis presents a frequency-domain feature quantification of the tonic component, named gradual changes. With it, we evaluate how the variation of the tonic component correlates with the player experience valence, in a set virtual reality games. The analysis also offers an evaluation of the prediction capabilities of the gradual changes, to classify player experience, using a supervised learning approach. Our results support the idea that the tonic component correlates with player experience, statistically demonstrating that a higher number of gradual changes feature a directly proportional relationship with a negative valence in player experience. Despite this, the gradual changes featured a rather limited prediction capability upon player experience, motivating future work in this area.

With the continuous development of extended reality (XR), encompassing augmented reality (AR), virtual reality (VR), and mixed reality (MR), the increasing application of VR, especially combined with game technology in the health area, is trending. Due to this development, academia and industry have rising research and practices focusing on VR exercise game applications and their evaluation. This paper presented a pilot study addressing the comparison of user preferences in using VR exercise games. Eight volunteering participants with VR or rowing experience were involved in the pilot study. Their responses on using different postures (standing or sitting), display devices (VR or a large screen), and game tasks (collect coins vs distance travelled) were explored, as well as feedback suggestions for the study and VR games. The pilot study revealed the opportunities and challenges to enhance the VR exercise games, user experience, and performance. It tested the feasibility and duration of each session and potential improvements that could be made for the main experiment, including the instructions, game environments, process, devices, data gathering, and analysis methods. © 2024 Copyright held by the owner/author(s).

Cartographic generalization aims at decreasing map or database detail. On one hand, its goal is taking into account map purpose, user constraints and needs, on the other hand maintaining and highlighting characteristic spatial patterns. One of the main challenges in the research concerning cartographic generalization is the evaluation of its results. While previous studies have exclusively concentrated on quantitative evaluation of cartographic generalization results, we complement these studies by considering both quantitative and qualitative evaluation with the map designers and map users. In this pilot study, six participants were asked to analyze both maps manually designed by experienced cartographers and mapsautomatically generalized with the use of selected machine learning and deep learning models, namely random forest (RF), deep learning (DL), decision trees (DT) and decision trees optimized with genetic algorithms (DTGA). Based on four tasks and two datasets containing: source settlements, manually (human) and automatically generalized ones to smaller scales the users had to identify important settlement patterns and judge if the result was machine or human design. The experiment was conducted with the use of a dedicated web application. Additionally, eye-tracking data were recorded using a Tobii X2-30 eye-tracker. The preliminary results, as shown in Figure 1, suggest that the generalization results that successfully keep the specific settlement patterns are: 1) the automated results (AI generalization) with the use of random forest (RF) and deep learning (DL), and 2) the reference atlas map, designed by experienced cartographers (human generalization). In this preliminary study, participants found the decision tree (DT) results the least successful for maintaining the specific settlement patterns.