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Intelligent Virtual Reality: AI-Driven Systems and Experiences
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Intelligent Virtual Reality: AI-Driven Systems and Experiences

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 May 2026 | Viewed by 1551

Special Issue Editors

Dr. Enrique Yeguas-Bolivar

E-Mail Website
Guest Editor
Computing and Numerical Analysis Department, University of Córdoba, 14014 Cordoba, Spain
Interests: virtual reality; augmented reality; artificial intelligence and inclusion; computer vision; computer graphics
Special Issues, Collections and Topics in MDPI journals
Dr. Mariana Buenestado Fernández

E-Mail Website
Guest Editor
Department of Education, University of Córdoba, 14014 Cordoba, Spain
Interests: educational technology, artificial intelligence in education, educational innovation; inclusive education; digital inclusion and equity; educational transformation and social impact
Special Issues, Collections and Topics in MDPI journals
Dr. Juri Taborri

E-Mail Website
Guest Editor
Department of Economics, Engineering, Society and Business Organization, University of Tuscia, 01100 Viterbo, Italy
Interests: virtual reality; artificial intelligence; metrology; inclusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, ‘Intelligent Virtual Reality: AI-Driven Systems and Experiences’, will explore the convergence of artificial intelligence and virtual reality, focusing on how AI can be used to enhance the design of immersive, adaptive, and inclusive environments. This Special Issue will highlight research on intelligent educational ecosystems that personalize learning experiences according to each learner’s level, pace, and cognitive profile, fostering engagement and improving learning outcomes. It will also emphasize accessibility in virtual environments, showcasing how AI can adapt interfaces, content, and interactions to the abilities of users with physical, sensory, or cognitive disabilities. Another central theme involves empathy and sensitivity training, where immersive simulations reproduce real-world scenarios of discrimination or inequality to promote inclusion, understanding, and emotional intelligence in educational and professional contexts.

This Special Issue welcomes a wide range of contributions, including, but not limited to, the following:

  • Adaptive and personalized learning environments in VR;
  • AI-driven accessibility solutions for inclusive virtual experiences;
  • Empathy and diversity training using immersive simulations;
  • Intelligent agents and virtual tutors in educational VR systems;
  • Emotion recognition and affective adaptation in VR environments;
  • Neuroadaptive and brain–computer interfaces for personalized VR;
  • Ethical and social implications of AI-powered virtual experiences;
  • Data-driven storytelling and generative content creation in VR;
  • Evaluation metrics and frameworks for AI-enhanced VR learning outcomes.

Dr. Enrique Yeguas-Bolivar
Dr. Mariana Buenestado Fernández
Dr. Juri Taborri
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-blind 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

  • artificial intelligence
  • virtual reality
  • adaptive learning
  • accessibility
  • empathy training
  • inclusive design
  • intelligent environments
  • affective computing
  • human–computer interaction
  • immersive experiences

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

23 pages, 3436 KB  
Article
Video-Based Quantitative Assessment of Upper Limb Impairments in Patients with Brain Lesions During Resistance Exercises
by Junjae Lee, Jihun Kim and Jaehyo Kim
Appl. Sci. 2026, 16(3), 1555; https://doi.org/10.3390/app16031555 - 4 Feb 2026
Viewed by 617
Abstract
This study proposes a video-based approach for quantitatively evaluating upper-limb joint abnormalities in individuals with brain lesions during resistance exercises. While the Fugl–Meyer Assessment (FMA) is a reliable clinical tool, its use is limited by the need for expert involvement and repeated assessments. [...] Read more.
This study proposes a video-based approach for quantitatively evaluating upper-limb joint abnormalities in individuals with brain lesions during resistance exercises. While the Fugl–Meyer Assessment (FMA) is a reliable clinical tool, its use is limited by the need for expert involvement and repeated assessments. To address this issue, skeletal joint data were extracted from RGB exercise videos using OpenPose, and joint abnormalities were identified by learning normal movement patterns from non-disabled participants. A total of 26 non-disabled individuals and 12 individuals with brain lesions performed chest press, shoulder press, and arm curl exercises. Joint movement patterns were analyzed using correlation analysis and a long short-term memory (LSTM) autoencoder. Only joints relevant to each exercise were evaluated, and joint-level results were integrated to compute arm-level abnormality rates. The correlation-based abnormality rate showed a significant negative correlation with FMA scores (r = −0.7789, p = 2.83 × 10−3), while the LSTM autoencoder-based abnormality rate exhibited a stronger correlation(r = −0.8454, p = 5.33 × 10−4). In addition, affected-side classification accuracy reached 78.0% and 83.3% for correlation analysis and the LSTM autoencoder, respectively. These results indicate that the proposed method is consistent with clinical assessments and can serve as a non-invasive, cost-effective tool for video-based rehabilitation evaluation. Full article
(This article belongs to the Special Issue Intelligent Virtual Reality: AI-Driven Systems and Experiences)
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12 pages, 1627 KB  
Article
Pneumatic Robot for Finger Rehabilitation After Stroke: A Pilot Validation on Short-Term Effectiveness Depending on FMA Score
by Jewheon Kang, Sion Seo, Hojin Jang and Jaehyo Kim
Appl. Sci. 2026, 16(2), 993; https://doi.org/10.3390/app16020993 - 19 Jan 2026
Viewed by 511
Abstract
Pneumatic soft robotic devices are emerging as promising tools for assisting hand rehabilitation in individuals with post-stroke motor impairment. However, evidence regarding their immediate functional impact remains limited, particularly across different impairment levels. This study presents a pilot validation of the YAD_V2 pneumatic [...] Read more.
Pneumatic soft robotic devices are emerging as promising tools for assisting hand rehabilitation in individuals with post-stroke motor impairment. However, evidence regarding their immediate functional impact remains limited, particularly across different impairment levels. This study presents a pilot validation of the YAD_V2 pneumatic finger rehabilitation robot and evaluates acute changes in finger range of motion (ROM) and task performance during a single intervention session. Twenty stroke participants were categorized into two groups based on the Fugl-Mayer Hand sub score: severe impairment (FMA-Hand < 10) and mild-to-moderate impairment (FMA-Hand ≥ 10). ROM was measured using integrated bending sensors during voluntary flexion–extension before, during, and after a 10-min pneumatic actuation session. A mixed 2 × 3 repeated-measure ANOVA revealed a significant Group × Time interaction (F(2, 36) = 4.628, p = 0.016, partial η2 = 0.205). In the severe group, ROM increased from 8.53° to 28.46° during actuation (p = 0.002), and partially returned to baseline afterward. In the mild–moderate group, no significant ROM changes were observed; however, cube-transfer time improved significantly (mean improvement: 0.88 s, p = 0.039). These findings indicate that pneumatic assistance induces distinct acute effects depending on impairment severity. This study provides preliminary evidence supporting the feasibility of the YAD_V2 robotic system and highlights the need for multi-session clinical trials to determine therapeutic efficacy. Full article
(This article belongs to the Special Issue Intelligent Virtual Reality: AI-Driven Systems and Experiences)
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