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Advances in AI for Extended Reality: From Explainable Agents to Generative Worlds

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 1050

Editors

Dr. Marina Anca Cidota

E-Mail Website
Guest Editor
Faculty of Mathematics and Computer Science, University of Bucharest, 030018 Bucharest, Romania
Interests: augmented reality; artificial intelligence; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Clara Maathuis

E-Mail Website
Guest Editor
Department of Computer Science, Open University of the Netherlands, 6419 AT Heerlen, The Netherlands
Interests: artificial intelligence; cyber security; cyber operations; information operations; military operations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The research work on the integration of artificial intelligence (AI) and extended reality (XR) has been widely explored, already proving its capacity to transform fields such as education, rehabilitation, entertainment, security, science, or industry by offering user-customizable and engaging experiences, skill augmentation, proper frame for better understanding in various fields, or remote assistance in collaborative environments.

Nevertheless, the recent advances in AI and the need to build trustworthy intelligent systems open up new perspectives for innovation at the intersection of AI and XR.

Therefore, we would like to propose this Special Issue, where we strongly encourage interdisciplinary applications that approach, but are not limited to, the following:

  • Explainable AI (XAI) techniques to make XR systems transparent, accountable, and trustworthy to ensure a good interaction between humans and intelligent systems.
  • Multiagent systems that bring scalability and realism to XR by allowing numerous AI entities to act simultaneously, interact with users and with each other, and collectively shape the virtual world.
  • LLMs and generative AI for XR to create immersive environments that are dynamic, adaptive, and capable of rich natural communication that feels more intuitive, personalized, and human-like.
  • Generative AI for XR to create multi-modal content, e.g., text, 3D assets, textures, animations, sounds, and even entire scenes, by transforming XR development from labor-intensive manual design into a collaborative process between humans and AI.

We invite authors to submit their research papers and review articles that are focused on the abovementioned topics.

Dr. Marina Anca Cidota
Dr. Clara Maathuis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • XAI techniques
  • generative AI
  • agent-based modeling
  • digital twin
  • augmented/virtual/mixed realities

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Published Papers (2 papers)

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Research

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14 pages, 419 KB  
Article
Extroversion–Introversion Rescheduler in Generative Agent via Few-Shot Prompting
by Sungwon Cho, Youngmin Ji and Yunsick Sung
Appl. Sci. 2026, 16(2), 883; https://doi.org/10.3390/app16020883 - 15 Jan 2026
Viewed by 388
Abstract
Generative Agent (GA) has emerged as a promising framework for simulating human-like behaviors. However, it is required for GA to generate a schedule that consistently reflects the agent’s E-I trait particularly in the extroversion–introversion (E-I) category to improve the realism of GA. We [...] Read more.
Generative Agent (GA) has emerged as a promising framework for simulating human-like behaviors. However, it is required for GA to generate a schedule that consistently reflects the agent’s E-I trait particularly in the extroversion–introversion (E-I) category to improve the realism of GA. We propose an E-I evaluation and rescheduling method that adjusts the agent’s schedule. Specifically, our method takes as input a one-hour schedule segmented into five-minute tasks and a corresponding E-I trait classified into seven degrees ranging from extremely high extroversion to extremely high introversion. Using the Evaluator powered by GPT-4o mini, each task is assessed for the alignment with the E-I traits. Each task that fails to meet a threshold is regenerated using few-shot prompting based on a collected successful schedule. This process is repeated until all tasks are aligned with the corresponding traits. Finally, the evaluator accesses the overall E-I consistency of the schedule that contains the tasks. Therefore, it is possible for the proposed method to enable E-I-consistent schedule generation in GA without retraining any models. In experiments, the proposed framework improved E-I alignment from an average of 14.7% to that of 78.4% with only 1.38 iterations on average, demonstrating both practical effectiveness and computational efficiency. Full article
(This article belongs to the Special Issue Advances in AI for Extended Reality: From Explainable Agents to Generative Worlds)
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21 pages, 990 KB  
Perspective
AI-Enhanced Extended Reality for Rehabilitation in Africa: A Perspective on Explainable Agents, Co-Creation, and Generative Worlds
by Chala Diriba Kenea and Bruno Bonnechère
Appl. Sci. 2026, 16(10), 4946; https://doi.org/10.3390/app16104946 - 15 May 2026
Viewed by 209
Abstract
The burden of disability is rising rapidly in Africa, where a severe shortage of rehabilitation professionals and limited infrastructure create a major treatment gap. Immersive virtual reality and serious games have shown promise for upper limb rehabilitation, but current extended reality (XR) solutions [...] Read more.
The burden of disability is rising rapidly in Africa, where a severe shortage of rehabilitation professionals and limited infrastructure create a major treatment gap. Immersive virtual reality and serious games have shown promise for upper limb rehabilitation, but current extended reality (XR) solutions lack personalization, cultural adaptability, real-time feedback, and scalability. This perspective paper proposes a conceptual AI-enhanced XR framework tailored to African low- and middle-income countries. We identify how generative AI, large language models, multiagent systems, and explainable AI can address specific rehabilitation barriers. The framework integrates these four pillars into a three-layer architecture covering content creation, interaction, and decision support. We analyze implementation considerations specific to African contexts—infrastructure, capacity building, cultural adaptation, ethics, and financing—and outline a detailed research agenda with near, medium, and longer term priorities. Realizing this vision requires co-design with African communities, investment in local capacity, adaptation to infrastructure constraints, and development of ethical frameworks. AI-enhanced XR has the potential to democratize access to quality rehabilitation across Africa, but this potential must be validated through rigorous, context-sensitive research. Full article
(This article belongs to the Special Issue Advances in AI for Extended Reality: From Explainable Agents to Generative Worlds)
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