Integrating Explainable Artificial Intelligence in Extended Reality Environments: A Systematic Survey
The integration of Artificial Intelligence (AI) within Extended Reality (XR) technologies has the potential to revolutionize user experiences by creating more immersive, interactive, and personalized environments. Nevertheless, the complexity and opacity of AI systems raise significant concerns rega...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/2/290 |
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| author | Clara Maathuis Marina Anca Cidota Dragoș Datcu Letiția Marin |
| author_facet | Clara Maathuis Marina Anca Cidota Dragoș Datcu Letiția Marin |
| author_sort | Clara Maathuis |
| collection | DOAJ |
| description | The integration of Artificial Intelligence (AI) within Extended Reality (XR) technologies has the potential to revolutionize user experiences by creating more immersive, interactive, and personalized environments. Nevertheless, the complexity and opacity of AI systems raise significant concerns regarding the transparency of data handling, reasoning processes, and decision-making mechanisms inherent in these technologies. To address these challenges, the implementation of explainable AI (XAI) methods and techniques becomes imperative, as they not only ensure compliance with prevailing ethical, social, and legal standards, norms, and principles, but also foster user trust and facilitate the broader adoption of AI solutions in XR applications. Despite the growing interest from both research and practitioner communities in this area, there is an important gap in the literature concerning a review of XAI methods specifically applied and tailored to XR systems. On this behalf, this research presents a systematic literature review that synthesizes current research on XAI approaches applied within the XR domain. Accordingly, this research aims to identify prevailing trends, assess the effectiveness of various XAI techniques, and highlight potential avenues for future research. It then contributes to the foundational understanding necessary for the development of transparent and trustworthy AI systems for XR systems using XAI technologies while enhancing the user experience and promoting responsible AI deployment. |
| format | Article |
| id | doaj-art-099576eaaeff482bbfcafc2c33b6ab92 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-099576eaaeff482bbfcafc2c33b6ab922025-01-24T13:40:03ZengMDPI AGMathematics2227-73902025-01-0113229010.3390/math13020290Integrating Explainable Artificial Intelligence in Extended Reality Environments: A Systematic SurveyClara Maathuis0Marina Anca Cidota1Dragoș Datcu2Letiția Marin3Department of Computer Science, Open University of The Netherlands, 6419 AT Heerlen, The NetherlandsDepartment of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, 010014 Bucharest, RomaniaIndependent Researcher, 2628 ZT Delft, The NetherlandsDepartment of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, 010014 Bucharest, RomaniaThe integration of Artificial Intelligence (AI) within Extended Reality (XR) technologies has the potential to revolutionize user experiences by creating more immersive, interactive, and personalized environments. Nevertheless, the complexity and opacity of AI systems raise significant concerns regarding the transparency of data handling, reasoning processes, and decision-making mechanisms inherent in these technologies. To address these challenges, the implementation of explainable AI (XAI) methods and techniques becomes imperative, as they not only ensure compliance with prevailing ethical, social, and legal standards, norms, and principles, but also foster user trust and facilitate the broader adoption of AI solutions in XR applications. Despite the growing interest from both research and practitioner communities in this area, there is an important gap in the literature concerning a review of XAI methods specifically applied and tailored to XR systems. On this behalf, this research presents a systematic literature review that synthesizes current research on XAI approaches applied within the XR domain. Accordingly, this research aims to identify prevailing trends, assess the effectiveness of various XAI techniques, and highlight potential avenues for future research. It then contributes to the foundational understanding necessary for the development of transparent and trustworthy AI systems for XR systems using XAI technologies while enhancing the user experience and promoting responsible AI deployment.https://www.mdpi.com/2227-7390/13/2/290explainable AIresponsible AItrustworthy AIextended realityaugmented realityvirtual reality |
| spellingShingle | Clara Maathuis Marina Anca Cidota Dragoș Datcu Letiția Marin Integrating Explainable Artificial Intelligence in Extended Reality Environments: A Systematic Survey Mathematics explainable AI responsible AI trustworthy AI extended reality augmented reality virtual reality |
| title | Integrating Explainable Artificial Intelligence in Extended Reality Environments: A Systematic Survey |
| title_full | Integrating Explainable Artificial Intelligence in Extended Reality Environments: A Systematic Survey |
| title_fullStr | Integrating Explainable Artificial Intelligence in Extended Reality Environments: A Systematic Survey |
| title_full_unstemmed | Integrating Explainable Artificial Intelligence in Extended Reality Environments: A Systematic Survey |
| title_short | Integrating Explainable Artificial Intelligence in Extended Reality Environments: A Systematic Survey |
| title_sort | integrating explainable artificial intelligence in extended reality environments a systematic survey |
| topic | explainable AI responsible AI trustworthy AI extended reality augmented reality virtual reality |
| url | https://www.mdpi.com/2227-7390/13/2/290 |
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