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Publications (3 of 3) Show all publications
Wang, C., Garro, V., Sundstedt, V., Hu, Y. & Goswami, P. (2026). Immersive Analytics Meets Artificial Intelligence: A Systematic Review. Computational Visual Media, 12(1), 1-34
Open this publication in new window or tab >>Immersive Analytics Meets Artificial Intelligence: A Systematic Review
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2026 (English)In: Computational Visual Media, ISSN 2096-0433, E-ISSN 2096-0662, Vol. 12, no 1, p. 1-34Article, review/survey (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
Tsinghua University Press, 2026
Keywords
artificial intelligence (AI), augmented reality (AR), immersive analytics (IA), virtual reality (VR), visual analytics (VA), visualization, Advanced Analytics, Artificial intelligence, Augmented reality, Data integration, Data visualization, Disaster prevention, Disasters, Engineering education, Integration, Urban planning, Virtual reality, Visual analytics, Analytic integration, Applications domains, Artificial intelligence techniques, Immersive, Immersive analytic, Visual analytic, Flow visualization
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:bth-29183 (URN)10.26599/CVM.2025.9450478 (DOI)001684103200008 ()2-s2.0-105029964479 (Scopus ID)
Funder
Knowledge Foundation, 20220068
Available from: 2026-02-25 Created: 2026-02-25 Last updated: 2026-02-25Bibliographically approved
Wang, C., Sundstedt, V. & Garro, V. (2025). Generative Artificial Intelligence for Immersive Analytics. In: Bashford-Rogers T., Meneveaux D., Ammi M., Ziat M., Jänicke S., Purchase H., Radeva P., Furnari A., Bouatouch K., Sousa A.A. (Ed.), Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: . Paper presented at 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2025, Porto, Feb 26-28, 2025 (pp. 938-946). SciTePress, 1
Open this publication in new window or tab >>Generative Artificial Intelligence for Immersive Analytics
2025 (English)In: Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications / [ed] Bashford-Rogers T., Meneveaux D., Ammi M., Ziat M., Jänicke S., Purchase H., Radeva P., Furnari A., Bouatouch K., Sousa A.A., SciTePress, 2025, Vol. 1, p. 938-946Conference paper, Published paper (Refereed)
Abstract [en]

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. 

Place, publisher, year, edition, pages
SciTePress, 2025
Series
VISIGRAPP, ISSN 2184-5921, E-ISSN 2184-4321
Keywords
Extended Reality, Generative Artificial Intelligence, Immersive Analytics, Visualization
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:bth-27748 (URN)10.5220/0013308400003912 (DOI)2-s2.0-105001960708 (Scopus ID)
Conference
20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2025, Porto, Feb 26-28, 2025
Funder
Knowledge Foundation, 20220068
Available from: 2025-04-22 Created: 2025-04-22 Last updated: 2025-09-30Bibliographically approved
Sundstedt, V., Hu, Y., Arlos, P., Abghari, S., Goswami, P., Tutschku, K., . . . Qin, B. (2025). Human-Centered Intelligent Realities Laboratory. In: Proceedings - 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025: . Paper presented at 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Saint-Malo, March 8-12, 2025. Institute of Electrical and Electronics Engineers (IEEE)
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2025 (English)In: Proceedings - 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025, Institute of Electrical and Electronics Engineers (IEEE), 2025Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2025
Keywords
Extended reality, artificial intelligence, intelligent reality, visualization, human-centered.
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-27756 (URN)10.1109/VRW66409.2025.00046 (DOI)001535113600040 ()2-s2.0-105005160909 (Scopus ID)9798331514846 (ISBN)
Conference
2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Saint-Malo, March 8-12, 2025
Funder
Knowledge Foundation, 20220068
Available from: 2025-04-23 Created: 2025-04-23 Last updated: 2025-10-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0009-0005-4979-6059

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