AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Behravan, M.; Matković, K.; Grǎcanin, D.
Generative AI for Context-Aware 3D Object Creation Using Vision-Language Models in Augmented Reality Proceedings Article
In: Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR, pp. 73–81, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331521578 (ISBN).
Abstract | Links | BibTeX | Tags: 3D object, 3D Object Generation, Artificial intelligence systems, Augmented Reality, Capture images, Context-Aware, Generative adversarial networks, Generative AI, generative artificial intelligence, Generative model, Language Model, Object creation, Vision language model, vision language models, Visual languages
@inproceedings{behravan_generative_2025,
title = {Generative AI for Context-Aware 3D Object Creation Using Vision-Language Models in Augmented Reality},
author = {M. Behravan and K. Matković and D. Grǎcanin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000292700&doi=10.1109%2FAIxVR63409.2025.00018&partnerID=40&md5=0a11897a4f37fd8ebaa257498cb92eb7},
doi = {10.1109/AIxVR63409.2025.00018},
isbn = {9798331521578 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},
pages = {73–81},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {We present a novel Artificial Intelligence (AI) system that functions as a designer assistant in augmented reality (AR) environments. Leveraging Vision Language Models (VLMs) like LLaVA and advanced text-to-3D generative models, users can capture images of their surroundings with an Augmented Reality (AR) headset. The system analyzes these images to recommend contextually relevant objects that enhance both functionality and visual appeal. The recommended objects are generated as 3D models and seamlessly integrated into the AR environment for interactive use. Our system utilizes open-source AI models running on local systems to enhance data security and reduce operational costs. Key features include context-aware object suggestions, optimal placement guidance, aesthetic matching, and an intuitive user interface for real-time interaction. Evaluations using the COCO 2017 dataset and real-world AR testing demonstrated high accuracy in object detection and contextual fit rating of 4.1 out of 5. By addressing the challenge of providing context-aware object recommendations in AR, our system expands the capabilities of AI applications in this domain. It enables users to create personalized digital spaces efficiently, leveraging AI for contextually relevant suggestions. © 2025 Elsevier B.V., All rights reserved.},
keywords = {3D object, 3D Object Generation, Artificial intelligence systems, Augmented Reality, Capture images, Context-Aware, Generative adversarial networks, Generative AI, generative artificial intelligence, Generative model, Language Model, Object creation, Vision language model, vision language models, Visual languages},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a novel Artificial Intelligence (AI) system that functions as a designer assistant in augmented reality (AR) environments. Leveraging Vision Language Models (VLMs) like LLaVA and advanced text-to-3D generative models, users can capture images of their surroundings with an Augmented Reality (AR) headset. The system analyzes these images to recommend contextually relevant objects that enhance both functionality and visual appeal. The recommended objects are generated as 3D models and seamlessly integrated into the AR environment for interactive use. Our system utilizes open-source AI models running on local systems to enhance data security and reduce operational costs. Key features include context-aware object suggestions, optimal placement guidance, aesthetic matching, and an intuitive user interface for real-time interaction. Evaluations using the COCO 2017 dataset and real-world AR testing demonstrated high accuracy in object detection and contextual fit rating of 4.1 out of 5. By addressing the challenge of providing context-aware object recommendations in AR, our system expands the capabilities of AI applications in this domain. It enables users to create personalized digital spaces efficiently, leveraging AI for contextually relevant suggestions. © 2025 Elsevier B.V., All rights reserved.
Casas, L.; Hannah, S.; Mitchell, K.
HoloJig: Interactive Spoken Prompt Specified Generative AI Environments Journal Article
In: IEEE Computer Graphics and Applications, vol. 45, no. 2, pp. 69–77, 2025, ISSN: 02721716 (ISSN); 15581756 (ISSN), (Publisher: IEEE Computer Society).
Abstract | Links | BibTeX | Tags: 3-D rendering, Article, Collaborative workspace, customer experience, Economic and social effects, generative artificial intelligence, human, Immersive, Immersive environment, parallax, Real- time, simulation, Simulation training, speech, Time based, Virtual environments, Virtual Reality, Virtual reality experiences, Virtual spaces, VR systems
@article{casas_holojig_2025,
title = {HoloJig: Interactive Spoken Prompt Specified Generative AI Environments},
author = {L. Casas and S. Hannah and K. Mitchell},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001182100&doi=10.1109%2FMCG.2025.3553780&partnerID=40&md5=9fafa25e4b6ddc9d2fe32d813fbabb20},
doi = {10.1109/MCG.2025.3553780},
issn = {02721716 (ISSN); 15581756 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Computer Graphics and Applications},
volume = {45},
number = {2},
pages = {69–77},
abstract = {HoloJig offers an interactive, speech-to-virtual reality (VR), VR experience that generates diverse environments in real time based on live spoken descriptions. Unlike traditional VR systems that rely on prebuilt assets, HoloJig dynamically creates personalized and immersive virtual spaces with depth-based parallax 3-D rendering, allowing users to define the characteristics of their immersive environment through verbal prompts. This generative approach opens up new possibilities for interactive experiences, including simulations, training, collaborative workspaces, and entertainment. In addition to speech-to-VR environment generation, a key innovation of HoloJig is its progressive visual transition mechanism, which smoothly dissolves between previously generated and newly requested environments, mitigating the delay caused by neural computations. This feature ensures a seamless and continuous user experience, even as new scenes are being rendered on remote servers. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: IEEE Computer Society},
keywords = {3-D rendering, Article, Collaborative workspace, customer experience, Economic and social effects, generative artificial intelligence, human, Immersive, Immersive environment, parallax, Real- time, simulation, Simulation training, speech, Time based, Virtual environments, Virtual Reality, Virtual reality experiences, Virtual spaces, VR systems},
pubstate = {published},
tppubtype = {article}
}
HoloJig offers an interactive, speech-to-virtual reality (VR), VR experience that generates diverse environments in real time based on live spoken descriptions. Unlike traditional VR systems that rely on prebuilt assets, HoloJig dynamically creates personalized and immersive virtual spaces with depth-based parallax 3-D rendering, allowing users to define the characteristics of their immersive environment through verbal prompts. This generative approach opens up new possibilities for interactive experiences, including simulations, training, collaborative workspaces, and entertainment. In addition to speech-to-VR environment generation, a key innovation of HoloJig is its progressive visual transition mechanism, which smoothly dissolves between previously generated and newly requested environments, mitigating the delay caused by neural computations. This feature ensures a seamless and continuous user experience, even as new scenes are being rendered on remote servers. © 2025 Elsevier B.V., All rights reserved.
Angelopoulos, J.; Manettas, C.; Alexopoulos, K.
Industrial Maintenance Optimization Based on the Integration of Large Language Models (LLM) and Augmented Reality (AR) Proceedings Article
In: K., Alexopoulos; S., Makris; P., Stavropoulos (Ed.): Lect. Notes Mech. Eng., pp. 197–205, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 21954356 (ISSN); 978-303186488-9 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Competition, Cost reduction, Critical path analysis, Crushed stone plants, Generative AI, generative artificial intelligence, Human expertise, Industrial equipment, Industrial maintenance, Language Model, Large language model, Maintenance, Maintenance optimization, Maintenance procedures, Manufacturing data processing, Potential errors, Problem oriented languages, Scheduled maintenance, Shopfloors, Solar power plants
@inproceedings{angelopoulos_industrial_2025,
title = {Industrial Maintenance Optimization Based on the Integration of Large Language Models (LLM) and Augmented Reality (AR)},
author = {J. Angelopoulos and C. Manettas and K. Alexopoulos},
editor = {Alexopoulos K. and Makris S. and Stavropoulos P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001421726&doi=10.1007%2f978-3-031-86489-6_20&partnerID=40&md5=63be31b9f4dda4aafd6a641630506c09},
doi = {10.1007/978-3-031-86489-6_20},
isbn = {21954356 (ISSN); 978-303186488-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Mech. Eng.},
pages = {197–205},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Traditional maintenance procedures often rely on manual data processing and human expertise, leading to inefficiencies and potential errors. In the context of Industry 4.0 several digital technologies, such as Artificial Intelligence (AI), Big Data Analytics (BDA), and eXtended Reality (XR) have been developed and are constantly being integrated in a plethora of manufacturing activities (including industrial maintenance), in an attempt to minimize human error, facilitate shop floor technicians, reduce costs as well as reduce equipment downtimes. The latest developments in the field of AI point towards Large Language Models (LLM) which can communicate with human operators in an intuitive manner. On the other hand, Augmented Reality, as part of XR technologies, offers useful functionalities for improving user perception and interaction with modern, complex industrial equipment. Therefore, the context of this research work lies in the development and training of an LLM in order to provide suggestions and actionable items for the mitigation of unforeseen events (e.g. equipment breakdowns), in order to facilitate shop-floor technicians during their everyday tasks. Paired with AR visualizations over the physical environment, the technicians will get instructions for performing tasks and checks on the industrial equipment in a manner similar to human-to-human communication. The functionality of the proposed framework extends to the integration of modules for exchanging information with the engineering department towards the scheduling of Maintenance and Repair Operations (MRO) as well as the creation of a repository of historical data in order to constantly retrain and optimize the LLM. © The Author(s) 2025.},
keywords = {Augmented Reality, Competition, Cost reduction, Critical path analysis, Crushed stone plants, Generative AI, generative artificial intelligence, Human expertise, Industrial equipment, Industrial maintenance, Language Model, Large language model, Maintenance, Maintenance optimization, Maintenance procedures, Manufacturing data processing, Potential errors, Problem oriented languages, Scheduled maintenance, Shopfloors, Solar power plants},
pubstate = {published},
tppubtype = {inproceedings}
}
Traditional maintenance procedures often rely on manual data processing and human expertise, leading to inefficiencies and potential errors. In the context of Industry 4.0 several digital technologies, such as Artificial Intelligence (AI), Big Data Analytics (BDA), and eXtended Reality (XR) have been developed and are constantly being integrated in a plethora of manufacturing activities (including industrial maintenance), in an attempt to minimize human error, facilitate shop floor technicians, reduce costs as well as reduce equipment downtimes. The latest developments in the field of AI point towards Large Language Models (LLM) which can communicate with human operators in an intuitive manner. On the other hand, Augmented Reality, as part of XR technologies, offers useful functionalities for improving user perception and interaction with modern, complex industrial equipment. Therefore, the context of this research work lies in the development and training of an LLM in order to provide suggestions and actionable items for the mitigation of unforeseen events (e.g. equipment breakdowns), in order to facilitate shop-floor technicians during their everyday tasks. Paired with AR visualizations over the physical environment, the technicians will get instructions for performing tasks and checks on the industrial equipment in a manner similar to human-to-human communication. The functionality of the proposed framework extends to the integration of modules for exchanging information with the engineering department towards the scheduling of Maintenance and Repair Operations (MRO) as well as the creation of a repository of historical data in order to constantly retrain and optimize the LLM. © The Author(s) 2025.
Wang, W. -S.; Lin, C. -J.; Lee, H. -Y.; Huang, Y. -M.; Wu, T. -T.
Integrating feedback mechanisms and ChatGPT for VR-based experiential learning: impacts on reflective thinking and AIoT physical hands-on tasks Journal Article
In: Interactive Learning Environments, vol. 33, no. 2, pp. 1770–1787, 2025, ISSN: 10494820 (ISSN), (Publisher: Routledge).
Abstract | Links | BibTeX | Tags: AIoT, feedback mechanisms, generative artificial intelligence, physical hands-on tasks, reflective thinking, Virtual Reality
@article{wang_integrating_2025,
title = {Integrating feedback mechanisms and ChatGPT for VR-based experiential learning: impacts on reflective thinking and AIoT physical hands-on tasks},
author = {W. -S. Wang and C. -J. Lin and H. -Y. Lee and Y. -M. Huang and T. -T. Wu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001238541&doi=10.1080%2F10494820.2024.2375644&partnerID=40&md5=d75343a9e5969482f384820424b7c58d},
doi = {10.1080/10494820.2024.2375644},
issn = {10494820 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Interactive Learning Environments},
volume = {33},
number = {2},
pages = {1770–1787},
abstract = {This study investigates the application of Virtual Reality (VR) in the educational field, particularly its integration with GAI technologies such as ChatGPT to enhance the learning experience. The research indicates that while VR provides an immersive learning environment fostering student interaction and interest, the lack of a structured learning framework and personalized feedback may limit its educational effectiveness and potentially affect the transfer of VR-learned knowledge to physical hands-on tasks. Hence, it calls for the provision of more targeted and personalized feedback in VR learning environments. Through a randomized controlled trial (RCT), this study collected data from 77 university students, integrating experiential learning in VR for acquiring AIoT knowledge and practical skills, and compared the effects of traditional feedback versus GPT feedback on promoting reflective thinking, learning motivation, cognitive levels, and AIoT hands-on abilities among the students. The results show that the group receiving GPT feedback significantly outperformed the control group across these learning indicators, demonstrating the effectiveness of GAI technologies in providing personalized learning support, facilitating deep learning, and enhancing educational outcomes. This study offers new insights into the integration of GAI technology in VR learning environments, paving new pathways for the development and application of future educational technologies. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Routledge},
keywords = {AIoT, feedback mechanisms, generative artificial intelligence, physical hands-on tasks, reflective thinking, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
This study investigates the application of Virtual Reality (VR) in the educational field, particularly its integration with GAI technologies such as ChatGPT to enhance the learning experience. The research indicates that while VR provides an immersive learning environment fostering student interaction and interest, the lack of a structured learning framework and personalized feedback may limit its educational effectiveness and potentially affect the transfer of VR-learned knowledge to physical hands-on tasks. Hence, it calls for the provision of more targeted and personalized feedback in VR learning environments. Through a randomized controlled trial (RCT), this study collected data from 77 university students, integrating experiential learning in VR for acquiring AIoT knowledge and practical skills, and compared the effects of traditional feedback versus GPT feedback on promoting reflective thinking, learning motivation, cognitive levels, and AIoT hands-on abilities among the students. The results show that the group receiving GPT feedback significantly outperformed the control group across these learning indicators, demonstrating the effectiveness of GAI technologies in providing personalized learning support, facilitating deep learning, and enhancing educational outcomes. This study offers new insights into the integration of GAI technology in VR learning environments, paving new pathways for the development and application of future educational technologies. © 2025 Elsevier B.V., All rights reserved.
Stacchio, L.; Balloni, E.; Frontoni, E.; Paolanti, M.; Zingaretti, P.; Pierdicca, R.
MineVRA: Exploring the Role of Generative AI-Driven Content Development in XR Environments through a Context-Aware Approach Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 5, pp. 3602–3612, 2025, ISSN: 10772626 (ISSN), (Publisher: IEEE Computer Society).
Abstract | Links | BibTeX | Tags: adult, Article, Artificial intelligence, Computer graphics, Computer vision, Content Development, Contents development, Context-Aware, Context-aware approaches, Extended reality, female, Generative adversarial networks, Generative AI, generative artificial intelligence, human, Human-in-the-loop, Immersive, Immersive environment, male, Multi-modal, User need, Virtual environments, Virtual Reality
@article{stacchio_minevra_2025,
title = {MineVRA: Exploring the Role of Generative AI-Driven Content Development in XR Environments through a Context-Aware Approach},
author = {L. Stacchio and E. Balloni and E. Frontoni and M. Paolanti and P. Zingaretti and R. Pierdicca},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003746367&doi=10.1109%2FTVCG.2025.3549160&partnerID=40&md5=3356eb968b3e6a0d3c9b75716b05fac4},
doi = {10.1109/TVCG.2025.3549160},
issn = {10772626 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
volume = {31},
number = {5},
pages = {3602–3612},
abstract = {The convergence of Artificial Intelligence (AI), Computer Vision (CV), Computer Graphics (CG), and Extended Reality (XR) is driving innovation in immersive environments. A key challenge in these environments is the creation of personalized 3D assets, traditionally achieved through manual modeling, a time-consuming process that often fails to meet individual user needs. More recently, Generative AI (GenAI) has emerged as a promising solution for automated, context-aware content generation. In this paper, we present MineVRA (Multimodal generative artificial iNtelligence for contExt-aware Virtual Reality Assets), a novel Human-In-The-Loop (HITL) XR framework that integrates GenAI to facilitate coherent and adaptive 3D content generation in immersive scenarios. To evaluate the effectiveness of this approach, we conducted a comparative user study analyzing the performance and user satisfaction of GenAI-generated 3D objects compared to those generated by Sketchfab in different immersive contexts. The results suggest that GenAI can significantly complement traditional 3D asset libraries, with valuable design implications for the development of human-centered XR environments. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: IEEE Computer Society},
keywords = {adult, Article, Artificial intelligence, Computer graphics, Computer vision, Content Development, Contents development, Context-Aware, Context-aware approaches, Extended reality, female, Generative adversarial networks, Generative AI, generative artificial intelligence, human, Human-in-the-loop, Immersive, Immersive environment, male, Multi-modal, User need, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The convergence of Artificial Intelligence (AI), Computer Vision (CV), Computer Graphics (CG), and Extended Reality (XR) is driving innovation in immersive environments. A key challenge in these environments is the creation of personalized 3D assets, traditionally achieved through manual modeling, a time-consuming process that often fails to meet individual user needs. More recently, Generative AI (GenAI) has emerged as a promising solution for automated, context-aware content generation. In this paper, we present MineVRA (Multimodal generative artificial iNtelligence for contExt-aware Virtual Reality Assets), a novel Human-In-The-Loop (HITL) XR framework that integrates GenAI to facilitate coherent and adaptive 3D content generation in immersive scenarios. To evaluate the effectiveness of this approach, we conducted a comparative user study analyzing the performance and user satisfaction of GenAI-generated 3D objects compared to those generated by Sketchfab in different immersive contexts. The results suggest that GenAI can significantly complement traditional 3D asset libraries, with valuable design implications for the development of human-centered XR environments. © 2025 Elsevier B.V., All rights reserved.
Chen, J.; Wu, X.; Lan, T.; Li, B.
LLMER: Crafting Interactive Extended Reality Worlds with JSON Data Generated by Large Language Models Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 5, pp. 2715–2724, 2025, ISSN: 10772626 (ISSN), (Publisher: IEEE Computer Society).
Abstract | Links | BibTeX | Tags: % reductions, 3D modeling, algorithm, Algorithms, Augmented Reality, Coding errors, Computer graphics, Computer interaction, computer interface, Computer simulation languages, Extended reality, generative artificial intelligence, human, Human users, human-computer interaction, Humans, Imaging, Immersive, Language, Language Model, Large language model, large language models, Metadata, Natural Language Processing, Natural language processing systems, Natural languages, procedures, Script generation, Spatio-temporal data, Three dimensional computer graphics, Three-Dimensional, three-dimensional imaging, User-Computer Interface, Virtual Reality
@article{chen_llmer_2025,
title = {LLMER: Crafting Interactive Extended Reality Worlds with JSON Data Generated by Large Language Models},
author = {J. Chen and X. Wu and T. Lan and B. Li},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003825793&doi=10.1109%2FTVCG.2025.3549549&partnerID=40&md5=50597473616678390f143a33082a13d3},
doi = {10.1109/TVCG.2025.3549549},
issn = {10772626 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
volume = {31},
number = {5},
pages = {2715–2724},
abstract = {The integration of Large Language Models (LLMs) like GPT-4 with Extended Reality (XR) technologies offers the potential to build truly immersive XR environments that interact with human users through natural language, e.g., generating and animating 3D scenes from audio inputs. However, the complexity of XR environments makes it difficult to accurately extract relevant contextual data and scene/object parameters from an overwhelming volume of XR artifacts. It leads to not only increased costs with pay-per-use models, but also elevated levels of generation errors. Moreover, existing approaches focusing on coding script generation are often prone to generation errors, resulting in flawed or invalid scripts, application crashes, and ultimately a degraded user experience. To overcome these challenges, we introduce LLMER, a novel framework that creates interactive XR worlds using JSON data generated by LLMs. Unlike prior approaches focusing on coding script generation, LLMER translates natural language inputs into JSON data, significantly reducing the likelihood of application crashes and processing latency. It employs a multi-stage strategy to supply only the essential contextual information adapted to the user's request and features multiple modules designed for various XR tasks. Our preliminary user study reveals the effectiveness of the proposed system, with over 80% reduction in consumed tokens and around 60% reduction in task completion time compared to state-of-the-art approaches. The analysis of users' feedback also illuminates a series of directions for further optimization. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: IEEE Computer Society},
keywords = {% reductions, 3D modeling, algorithm, Algorithms, Augmented Reality, Coding errors, Computer graphics, Computer interaction, computer interface, Computer simulation languages, Extended reality, generative artificial intelligence, human, Human users, human-computer interaction, Humans, Imaging, Immersive, Language, Language Model, Large language model, large language models, Metadata, Natural Language Processing, Natural language processing systems, Natural languages, procedures, Script generation, Spatio-temporal data, Three dimensional computer graphics, Three-Dimensional, three-dimensional imaging, User-Computer Interface, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The integration of Large Language Models (LLMs) like GPT-4 with Extended Reality (XR) technologies offers the potential to build truly immersive XR environments that interact with human users through natural language, e.g., generating and animating 3D scenes from audio inputs. However, the complexity of XR environments makes it difficult to accurately extract relevant contextual data and scene/object parameters from an overwhelming volume of XR artifacts. It leads to not only increased costs with pay-per-use models, but also elevated levels of generation errors. Moreover, existing approaches focusing on coding script generation are often prone to generation errors, resulting in flawed or invalid scripts, application crashes, and ultimately a degraded user experience. To overcome these challenges, we introduce LLMER, a novel framework that creates interactive XR worlds using JSON data generated by LLMs. Unlike prior approaches focusing on coding script generation, LLMER translates natural language inputs into JSON data, significantly reducing the likelihood of application crashes and processing latency. It employs a multi-stage strategy to supply only the essential contextual information adapted to the user's request and features multiple modules designed for various XR tasks. Our preliminary user study reveals the effectiveness of the proposed system, with over 80% reduction in consumed tokens and around 60% reduction in task completion time compared to state-of-the-art approaches. The analysis of users' feedback also illuminates a series of directions for further optimization. © 2025 Elsevier B.V., All rights reserved.
Sousa, R. T.; Oliveira, E. A. M.; Cintra, L. M. F.; Filho, A. R. G. Galvão
Transformative Technologies for Rehabilitation: Leveraging Immersive and AI-Driven Solutions to Reduce Recidivism and Promote Decent Work Proceedings Article
In: Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW, pp. 168–171, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331514846 (ISBN).
Abstract | Links | BibTeX | Tags: AI- Driven Rehabilitation, Artificial intelligence- driven rehabilitation, Emotional intelligence, Engineering education, Generative AI, generative artificial intelligence, Immersive, Immersive technologies, Immersive Technology, Language Model, Large language model, large language models, Skills development, Social Reintegration, Social skills, Sociology, Vocational training
@inproceedings{sousa_transformative_2025,
title = {Transformative Technologies for Rehabilitation: Leveraging Immersive and AI-Driven Solutions to Reduce Recidivism and Promote Decent Work},
author = {R. T. Sousa and E. A. M. Oliveira and L. M. F. Cintra and A. R. G. Galvão Filho},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005140551&doi=10.1109%2FVRW66409.2025.00042&partnerID=40&md5=a8dbe15493fd8361602d049f2b09efe3},
doi = {10.1109/VRW66409.2025.00042},
isbn = {9798331514846 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},
pages = {168–171},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The reintegration of incarcerated individuals into society presents significant challenges, particularly in addressing barriers related to vocational training, social skill development, and emotional rehabilitation. Immersive technologies, such as Virtual Reality and Augmented Reality, combined with generative Artificial Intelligence (AI) and Large Language Models, offer innovative opportunities to enhance these areas. These technologies create practical, controlled environments for skill acquisition and behavioral training, while generative AI enables dynamic, personalized, and adaptive experiences. This paper explores the broader potential of these integrated technologies in supporting rehabilitation, reducing recidivism, and fostering sustainable employment opportunities and these initiatives align with the overarching equity objective of ensuring Decent Work for All, reinforcing the commitment to inclusive and equitable progress across diverse communities, through the transformative potential of immersive and AI-driven systems in correctional systems. © 2025 Elsevier B.V., All rights reserved.},
keywords = {AI- Driven Rehabilitation, Artificial intelligence- driven rehabilitation, Emotional intelligence, Engineering education, Generative AI, generative artificial intelligence, Immersive, Immersive technologies, Immersive Technology, Language Model, Large language model, large language models, Skills development, Social Reintegration, Social skills, Sociology, Vocational training},
pubstate = {published},
tppubtype = {inproceedings}
}
The reintegration of incarcerated individuals into society presents significant challenges, particularly in addressing barriers related to vocational training, social skill development, and emotional rehabilitation. Immersive technologies, such as Virtual Reality and Augmented Reality, combined with generative Artificial Intelligence (AI) and Large Language Models, offer innovative opportunities to enhance these areas. These technologies create practical, controlled environments for skill acquisition and behavioral training, while generative AI enables dynamic, personalized, and adaptive experiences. This paper explores the broader potential of these integrated technologies in supporting rehabilitation, reducing recidivism, and fostering sustainable employment opportunities and these initiatives align with the overarching equity objective of ensuring Decent Work for All, reinforcing the commitment to inclusive and equitable progress across diverse communities, through the transformative potential of immersive and AI-driven systems in correctional systems. © 2025 Elsevier B.V., All rights reserved.
Grubert, J.; Schmalstieg, D.; Dickhaut, K.
Towards Supporting Literary Studies Using Virtual Reality and Generative Artificial Intelligence Proceedings Article
In: Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW, pp. 147–149, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331514846 (ISBN).
Abstract | Links | BibTeX | Tags: Cultural-historical, generative artificial intelligence, Immersive, literary studies, Literary study, Literary texts, Literature analysis, Textual-analysis, Virtual Reality, Visual elements
@inproceedings{grubert_towards_2025,
title = {Towards Supporting Literary Studies Using Virtual Reality and Generative Artificial Intelligence},
author = {J. Grubert and D. Schmalstieg and K. Dickhaut},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005144426&doi=10.1109%2FVRW66409.2025.00037&partnerID=40&md5=2a1f62e6193cb0a54105d77c5ba85aa9},
doi = {10.1109/VRW66409.2025.00037},
isbn = {9798331514846 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},
pages = {147–149},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Literary studies critically examine fictional texts, exploring their structures, themes, stylistic features, and cultural-historical contexts. A central challenge in this field lies in bridging textual analysis with the spatial and sensory dimensions of settings described or implied in texts. Traditional methodologies often require scholars to mentally reconstruct these environments, leading to incomplete or inconsistent interpretations. Readers may be biased by their personal context or experiences, or may lack detailed knowledge of the relevant historical facts. This paper argues for the integration of virtual reality and generative artificial intelligence as supporting instruments to enhance literary research. The former enables immersive, spatially accurate reconstructions of historical environments, while the latter provides tools such as text-to-image and text-to-3D generation which let us dynamically render visual elements quoted in literary texts. Together, these technologies have the potential to significantly enhance traditional literature analysis methodologies, enabling novel approaches for contextualizing and analyzing literature in its spatial and cultural milieu. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Cultural-historical, generative artificial intelligence, Immersive, literary studies, Literary study, Literary texts, Literature analysis, Textual-analysis, Virtual Reality, Visual elements},
pubstate = {published},
tppubtype = {inproceedings}
}
Literary studies critically examine fictional texts, exploring their structures, themes, stylistic features, and cultural-historical contexts. A central challenge in this field lies in bridging textual analysis with the spatial and sensory dimensions of settings described or implied in texts. Traditional methodologies often require scholars to mentally reconstruct these environments, leading to incomplete or inconsistent interpretations. Readers may be biased by their personal context or experiences, or may lack detailed knowledge of the relevant historical facts. This paper argues for the integration of virtual reality and generative artificial intelligence as supporting instruments to enhance literary research. The former enables immersive, spatially accurate reconstructions of historical environments, while the latter provides tools such as text-to-image and text-to-3D generation which let us dynamically render visual elements quoted in literary texts. Together, these technologies have the potential to significantly enhance traditional literature analysis methodologies, enabling novel approaches for contextualizing and analyzing literature in its spatial and cultural milieu. © 2025 Elsevier B.V., All rights reserved.
Wei, X.; Chen, Y.; Zhao, P.; Wang, L.; Lee, L. -K.; Liu, R.
In: Interactive Learning Environments, 2025, ISSN: 10494820 (ISSN), (Publisher: Routledge).
Abstract | Links | BibTeX | Tags: 5E learning model, generative artificial intelligence, Immersive virtual reality, Pedagogical agents, primary students, Science education
@article{wei_effects_2025,
title = {Effects of immersive virtual reality on primary students’ science performance in classroom settings: a generative AI pedagogical agents-enhanced 5E approach},
author = {X. Wei and Y. Chen and P. Zhao and L. Wang and L. -K. Lee and R. Liu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007642085&doi=10.1080%2F10494820.2025.2514101&partnerID=40&md5=cf1d1633ff4e6f78f2b22252ecb3c3b9},
doi = {10.1080/10494820.2025.2514101},
issn = {10494820 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Interactive Learning Environments},
abstract = {Immersive virtual reality (IVR) holds the potential to transform science education by offering opportunities to enhance learners’ engagement, motivation, and conceptual understanding. However, the integration of generative AI pedagogical agents (GPAs) into IVR environments remains underexplored. Specifically, the application of GPAs as a scaffold within the framework of the 5E learning model in science education has not been fully examined. To address these gaps, this study explored the impact of a GPA-enhanced 5E (GPA-5E) learning approach in IVR on primary students’ academic achievement, self-efficacy, collective efficacy, and their perceptions of the proposed method. Adopting a mixed-methods design, eighty sixth-grade students from two complete classes were assigned to either an experimental group engaging IVR science learning with a GPA-5E approach or a control group following the traditional 5E method. The results indicated that the GPA-5E approach in IVR science learning significantly improved students’ academic achievement, self-efficacy, and collective efficacy compared to the traditional method. Students in the experimental group also reported positive perceptions of the GPA-5E method, emphasizing its benefits in IVR science learning. These findings underscore the potential of integrating GPA-enhanced scaffolds within IVR environments to enrich pedagogical strategies and improve student outcomes in science education. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Routledge},
keywords = {5E learning model, generative artificial intelligence, Immersive virtual reality, Pedagogical agents, primary students, Science education},
pubstate = {published},
tppubtype = {article}
}
Immersive virtual reality (IVR) holds the potential to transform science education by offering opportunities to enhance learners’ engagement, motivation, and conceptual understanding. However, the integration of generative AI pedagogical agents (GPAs) into IVR environments remains underexplored. Specifically, the application of GPAs as a scaffold within the framework of the 5E learning model in science education has not been fully examined. To address these gaps, this study explored the impact of a GPA-enhanced 5E (GPA-5E) learning approach in IVR on primary students’ academic achievement, self-efficacy, collective efficacy, and their perceptions of the proposed method. Adopting a mixed-methods design, eighty sixth-grade students from two complete classes were assigned to either an experimental group engaging IVR science learning with a GPA-5E approach or a control group following the traditional 5E method. The results indicated that the GPA-5E approach in IVR science learning significantly improved students’ academic achievement, self-efficacy, and collective efficacy compared to the traditional method. Students in the experimental group also reported positive perceptions of the GPA-5E method, emphasizing its benefits in IVR science learning. These findings underscore the potential of integrating GPA-enhanced scaffolds within IVR environments to enrich pedagogical strategies and improve student outcomes in science education. © 2025 Elsevier B.V., All rights reserved.
Yokoyama, N.; Kimura, R.; Nakajima, T.
ViGen: Defamiliarizing Everyday Perception for Discovering Unexpected Insights Proceedings Article
In: H., Degen; S., Ntoa (Ed.): Lect. Notes Comput. Sci., pp. 397–417, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303193417-9 (ISBN).
Abstract | Links | BibTeX | Tags: Artful Expression, Artistic technique, Augmented Reality, Daily lives, Defamiliarization, Dynamic environments, Engineering education, Enhanced vision systems, Generative AI, generative artificial intelligence, Human augmentation, Human engineering, Human-AI Interaction, Human-artificial intelligence interaction, Semi-transparent
@inproceedings{yokoyama_vigen_2025,
title = {ViGen: Defamiliarizing Everyday Perception for Discovering Unexpected Insights},
author = {N. Yokoyama and R. Kimura and T. Nakajima},
editor = {Degen H. and Ntoa S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007760030&doi=10.1007%2f978-3-031-93418-6_26&partnerID=40&md5=dee6f54688284313a45579aab5f934d6},
doi = {10.1007/978-3-031-93418-6_26},
isbn = {03029743 (ISSN); 978-303193417-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15821 LNAI},
pages = {397–417},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This paper proposes ViGen, an Augmented Reality (AR) and Artificial Intelligence (AI)-enhanced vision system designed to facilitate defamiliarization in daily life. Humans rely on sight to gather information, think, and act, yet the act of seeing often becomes passive in daily life. Inspired by Victor Shklovsky’s concept of defamiliarization and the artistic technique of photomontage, ViGen seeks to disrupt habitual perceptions. It achieves this by overlaying semi-transparent, AI-generated images, created based on the user’s view, through an AR display. The system is evaluated by several structured interviews, in which participants experience ViGen in three different scenarios. Results indicate that AI-generated visuals effectively supported defamiliarization by transforming ordinary scenes into unfamiliar ones. However, the user’s familiarity with a place plays a significant role. Also, while the feature that adjusts the transparency of overlaid images enhances safety, its limitations in dynamic environments suggest the need for further research across diverse cultural and geographic contexts. This study demonstrates the potential of AI-augmented vision systems to stimulate new ways of seeing, offering insights for further development in visual augmentation technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Artful Expression, Artistic technique, Augmented Reality, Daily lives, Defamiliarization, Dynamic environments, Engineering education, Enhanced vision systems, Generative AI, generative artificial intelligence, Human augmentation, Human engineering, Human-AI Interaction, Human-artificial intelligence interaction, Semi-transparent},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes ViGen, an Augmented Reality (AR) and Artificial Intelligence (AI)-enhanced vision system designed to facilitate defamiliarization in daily life. Humans rely on sight to gather information, think, and act, yet the act of seeing often becomes passive in daily life. Inspired by Victor Shklovsky’s concept of defamiliarization and the artistic technique of photomontage, ViGen seeks to disrupt habitual perceptions. It achieves this by overlaying semi-transparent, AI-generated images, created based on the user’s view, through an AR display. The system is evaluated by several structured interviews, in which participants experience ViGen in three different scenarios. Results indicate that AI-generated visuals effectively supported defamiliarization by transforming ordinary scenes into unfamiliar ones. However, the user’s familiarity with a place plays a significant role. Also, while the feature that adjusts the transparency of overlaid images enhances safety, its limitations in dynamic environments suggest the need for further research across diverse cultural and geographic contexts. This study demonstrates the potential of AI-augmented vision systems to stimulate new ways of seeing, offering insights for further development in visual augmentation technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Monjoree, U.; Yan, W.
Assessing AI Models' Spatial Visualization in PSVT:R and Augmented Reality: Towards Enhancing AI's Spatial Intelligence Proceedings Article
In: pp. 727–734, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331524005 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, Architecture engineering, Artificial intelligence, Augmented Reality, Construction science, Engineering education, Engineering science, Generative AI, generative artificial intelligence, Image processing, Intelligence models, Linear transformations, Medicine, Rotation, Rotation process, Spatial Intelligence, Spatial rotation, Spatial visualization, Three dimensional computer graphics, Three dimensional space, Visualization
@inproceedings{monjoree_assessing_2025,
title = {Assessing AI Models' Spatial Visualization in PSVT:R and Augmented Reality: Towards Enhancing AI's Spatial Intelligence},
author = {U. Monjoree and W. Yan},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011255775&doi=10.1109%2FCAI64502.2025.00131&partnerID=40&md5=0bd551863839b3025898e55265403969},
doi = {10.1109/CAI64502.2025.00131},
isbn = {9798331524005 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {727–734},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Spatial intelligence is important in many fields, such as Architecture, Engineering, and Construction (AEC), Science, Technology, Engineering, and Mathematics (STEM), and Medicine. Understanding three-dimensional (3D) spatial rotations can involve verbal descriptions and visual or interactive examples, illustrating how objects move and change orientation in 3D space. Recent studies show that artificial intelligence (AI) with language and vision capabilities still faces limitations in spatial reasoning. In this paper, we have studied the spatial capabilities of advanced generative AI to understand the rotations of objects in 3D space utilizing its image processing and language processing features. We examined the spatial intelligence of three generative AI models (GPT-4, Gemini 1.5 Pro, and Llama 3.2) to understand the spatial rotation process with spatial rotation diagrams based on the revised Purdue Spatial Visualization Test: Visualization of Rotations (Revised PSVT:R). Furthermore, we incorporated an added layer of a coordinate system axes on Revised PSVT:R to study the variations in generative AI models' performance. We additionally examined generative AI models' understanding of 3D rotations in Augmented Reality (AR) scene images that visualize spatial rotations of a physical object in 3D space and observed an increased accuracy of generative AI models' understanding of rotations by adding additional textual information depicting the rotation process or mathematical representations of the rotation (e.g., matrices) superimposed on the object. The results indicate that while GPT-4, Gemini 1.5 Pro, and Llama 3.2 as the main current generative AI model lack the understanding of a spatial rotation process, it has the potential to understand the rotation process with additional information that can be provided by methods such as AR. AR can superimpose textual information or mathematical representations of rotations on spatial transformation diagrams and create a more intelligible input for AI to comprehend or for training AI's spatial intelligence. Furthermore, by combining the potentials in spatial intelligence of AI with AR's interactive visualization abilities, we expect to offer enhanced guidance for students' spatial learning activities. Such spatial guidance can greatly benefit understanding spatial transformations and additionally support processes like assembly, construction, manufacturing, as well as learning in AEC, STEM, and Medicine that require precise 3D spatial understanding. © 2025 Elsevier B.V., All rights reserved.},
keywords = {3D modeling, Architecture engineering, Artificial intelligence, Augmented Reality, Construction science, Engineering education, Engineering science, Generative AI, generative artificial intelligence, Image processing, Intelligence models, Linear transformations, Medicine, Rotation, Rotation process, Spatial Intelligence, Spatial rotation, Spatial visualization, Three dimensional computer graphics, Three dimensional space, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Spatial intelligence is important in many fields, such as Architecture, Engineering, and Construction (AEC), Science, Technology, Engineering, and Mathematics (STEM), and Medicine. Understanding three-dimensional (3D) spatial rotations can involve verbal descriptions and visual or interactive examples, illustrating how objects move and change orientation in 3D space. Recent studies show that artificial intelligence (AI) with language and vision capabilities still faces limitations in spatial reasoning. In this paper, we have studied the spatial capabilities of advanced generative AI to understand the rotations of objects in 3D space utilizing its image processing and language processing features. We examined the spatial intelligence of three generative AI models (GPT-4, Gemini 1.5 Pro, and Llama 3.2) to understand the spatial rotation process with spatial rotation diagrams based on the revised Purdue Spatial Visualization Test: Visualization of Rotations (Revised PSVT:R). Furthermore, we incorporated an added layer of a coordinate system axes on Revised PSVT:R to study the variations in generative AI models' performance. We additionally examined generative AI models' understanding of 3D rotations in Augmented Reality (AR) scene images that visualize spatial rotations of a physical object in 3D space and observed an increased accuracy of generative AI models' understanding of rotations by adding additional textual information depicting the rotation process or mathematical representations of the rotation (e.g., matrices) superimposed on the object. The results indicate that while GPT-4, Gemini 1.5 Pro, and Llama 3.2 as the main current generative AI model lack the understanding of a spatial rotation process, it has the potential to understand the rotation process with additional information that can be provided by methods such as AR. AR can superimpose textual information or mathematical representations of rotations on spatial transformation diagrams and create a more intelligible input for AI to comprehend or for training AI's spatial intelligence. Furthermore, by combining the potentials in spatial intelligence of AI with AR's interactive visualization abilities, we expect to offer enhanced guidance for students' spatial learning activities. Such spatial guidance can greatly benefit understanding spatial transformations and additionally support processes like assembly, construction, manufacturing, as well as learning in AEC, STEM, and Medicine that require precise 3D spatial understanding. © 2025 Elsevier B.V., All rights reserved.
Shi, L.; Gu, Y.; Zheng, Y.; Kameda, S.; Lu, H.
LWD-IUM: A Lightweight Detector for Advancing Robotic Grasp in VR-Based Industrial and Underwater Metaverse Proceedings Article
In: pp. 1384–1391, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331508876 (ISBN).
Abstract | Links | BibTeX | Tags: 3D object, 3D object detection, Deep learning, generative artificial intelligence, Grasping and manipulation, Intelligent robots, Learning systems, Metaverses, Neural Networks, Object Detection, Object recognition, Objects detection, Real- time, Real-time, Robotic grasping, robotic grasping and manipulation, Robotic manipulation, Virtual Reality, Vision transformer, Visual servoing
@inproceedings{shi_lwd-ium_2025,
title = {LWD-IUM: A Lightweight Detector for Advancing Robotic Grasp in VR-Based Industrial and Underwater Metaverse},
author = {L. Shi and Y. Gu and Y. Zheng and S. Kameda and H. Lu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011354353&doi=10.1109%2FIWCMC65282.2025.11059637&partnerID=40&md5=77aa4cdb0a08a1db5d0027a71403da89},
doi = {10.1109/IWCMC65282.2025.11059637},
isbn = {9798331508876 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {1384–1391},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In the burgeoning field of virtual reality (VR) metaverse, the sophistication of interactions between robotic agents and their environment has become a critical concern. In this work, we present LWD-IUM, a novel light-weight detector designed to enhance robotic grasp capabilities in the VR metaverse. LWD-IUM applies deep learning techniques to discern and navigate the complex VR metaverse environment, aiding robotic agents in the identification and grasping of objects with high precision and efficiency. The algorithm is constructed with an advanced lightweight neural network structure based on self-attention mechanism that ensures optimal balance between computational cost and performance, making it highly suitable for real-time applications in VR. Evaluation on the KITTI 3D dataset demonstrated real-time detection capabilities (24-30 fps) of LWD-IUM, with its mean average precision (mAP) remaining 80% above standard 3D detectors, even with a 50% parameter reduction. In addition, we show that LWD-IUM outperforms existing models for object detection and grasping tasks through the real environment testing on a Baxter dual-arm collaborative robot. By pioneering advancements in robotic grasp in the VR metaverse, LWD-IUM promotes more immersive and realistic interactions, pushing the boundaries of what's possible in virtual experiences. © 2025 Elsevier B.V., All rights reserved.},
keywords = {3D object, 3D object detection, Deep learning, generative artificial intelligence, Grasping and manipulation, Intelligent robots, Learning systems, Metaverses, Neural Networks, Object Detection, Object recognition, Objects detection, Real- time, Real-time, Robotic grasping, robotic grasping and manipulation, Robotic manipulation, Virtual Reality, Vision transformer, Visual servoing},
pubstate = {published},
tppubtype = {inproceedings}
}
In the burgeoning field of virtual reality (VR) metaverse, the sophistication of interactions between robotic agents and their environment has become a critical concern. In this work, we present LWD-IUM, a novel light-weight detector designed to enhance robotic grasp capabilities in the VR metaverse. LWD-IUM applies deep learning techniques to discern and navigate the complex VR metaverse environment, aiding robotic agents in the identification and grasping of objects with high precision and efficiency. The algorithm is constructed with an advanced lightweight neural network structure based on self-attention mechanism that ensures optimal balance between computational cost and performance, making it highly suitable for real-time applications in VR. Evaluation on the KITTI 3D dataset demonstrated real-time detection capabilities (24-30 fps) of LWD-IUM, with its mean average precision (mAP) remaining 80% above standard 3D detectors, even with a 50% parameter reduction. In addition, we show that LWD-IUM outperforms existing models for object detection and grasping tasks through the real environment testing on a Baxter dual-arm collaborative robot. By pioneering advancements in robotic grasp in the VR metaverse, LWD-IUM promotes more immersive and realistic interactions, pushing the boundaries of what's possible in virtual experiences. © 2025 Elsevier B.V., All rights reserved.
Paterakis, I.; Manoudaki, N.
Osmosis: Generative AI and XR for the real-time transformation of urban architectural environments Journal Article
In: International Journal of Architectural Computing, 2025, ISSN: 14780771 (ISSN), (Publisher: SAGE Publications Inc.).
Abstract | Links | BibTeX | Tags: Architectural design, Architectural environment, Artificial intelligence, Biodigital design, Case-studies, Computational architecture, Computer architecture, Extended reality, generative artificial intelligence, Immersive, Immersive environment, immersive environments, Natural language processing systems, Real- time, Urban environments, urban planning
@article{paterakis_osmosis_2025,
title = {Osmosis: Generative AI and XR for the real-time transformation of urban architectural environments},
author = {I. Paterakis and N. Manoudaki},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105014516125&doi=10.1177%2F14780771251356526&partnerID=40&md5=4bbcb09440d91899cb7d2d5d0c852507},
doi = {10.1177/14780771251356526},
issn = {14780771 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Architectural Computing},
abstract = {This work contributes to the evolving discourse on biodigital architecture by examining how generative artificial intelligence (AI) and extended reality (XR) systems can be combined to create immersive urban environments. Focusing on the case study of “Osmosis”, a series of large-scale public installations, this work proposes a methodological framework for real-time architectural composition in XR using diffusion models and interaction. The project reframes the architectural façade as a semi permeable membrane, through which digital content diffuses in response to environmental and user inputs. By integrating natural language prompts, multimodal input, and AI-generated visual synthesis with projection mapping, Osmosis advances a vision for urban architecture that is interactive, data-driven, and sensorially rich. The work explores new design territories where stochastic form-making and real-time responsiveness intersect, and positions AI as an augmentation of architectural creativity rather than its replacement. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: SAGE Publications Inc.},
keywords = {Architectural design, Architectural environment, Artificial intelligence, Biodigital design, Case-studies, Computational architecture, Computer architecture, Extended reality, generative artificial intelligence, Immersive, Immersive environment, immersive environments, Natural language processing systems, Real- time, Urban environments, urban planning},
pubstate = {published},
tppubtype = {article}
}
This work contributes to the evolving discourse on biodigital architecture by examining how generative artificial intelligence (AI) and extended reality (XR) systems can be combined to create immersive urban environments. Focusing on the case study of “Osmosis”, a series of large-scale public installations, this work proposes a methodological framework for real-time architectural composition in XR using diffusion models and interaction. The project reframes the architectural façade as a semi permeable membrane, through which digital content diffuses in response to environmental and user inputs. By integrating natural language prompts, multimodal input, and AI-generated visual synthesis with projection mapping, Osmosis advances a vision for urban architecture that is interactive, data-driven, and sensorially rich. The work explores new design territories where stochastic form-making and real-time responsiveness intersect, and positions AI as an augmentation of architectural creativity rather than its replacement. © 2025 Elsevier B.V., All rights reserved.
Suzuki, R.; Abtahi, P.; Zhu-Tian, C.; Dogan, M. D.; Colaço, A.; Gonzalez, E. J.; Ahuja, K.; González-Franco, M.
Programmable reality Journal Article
In: Frontiers in Virtual Reality, vol. 6, 2025, ISSN: 26734192 (ISSN), (Publisher: Frontiers Media SA).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Extended reality, generative artificial intelligence, Virtual Reality
@article{suzuki_programmable_2025,
title = {Programmable reality},
author = {R. Suzuki and P. Abtahi and C. Zhu-Tian and M. D. Dogan and A. Colaço and E. J. Gonzalez and K. Ahuja and M. González-Franco},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018040602&doi=10.3389%2Ffrvir.2025.1649785&partnerID=40&md5=798661abad02a17f4663f766918b8809},
doi = {10.3389/frvir.2025.1649785},
issn = {26734192 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Frontiers in Virtual Reality},
volume = {6},
abstract = {Innovations in spatial computing and artificial intelligence (AI) are making it possible to overlay dynamic, interactive digital elements on the physical world. Soon, every object might have a real-time digital twin, enabling the “Internet of Things” so as to identify and interact with even unconnected items. This programmable reality would enable computational manipulation of the world around us through alteration of its appearance or functionality, similar to software, but for reality itself. Advances in AI language models have enabled zero-shot segmentation and understanding of the world, making it possible to query and manipulate objects with precision. However, this vision also demands natural and intuitive ways for humans to interact with these models through gestures, gaze, and existing devices. Augmented reality (AR) provides the ideal bridge between AI output and human input in the physical world. Moreover, diffusion models and physics simulations offer exciting possibilities for content generation and editing, allowing us to transform everyday activities into extraordinary experiences. As AR devices become ubiquitous and indistinguishable from reality, these technologies blur the lines between reality and simulations. This raises profound questions about how we perceive and experience the world while having implications for memory, learning, and even behavior. Programmable reality enabled by AR and AI has vast potential to reshape our relationships with the digital realm, ultimately making it an extension of the physical realm. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Frontiers Media SA},
keywords = {Artificial intelligence, Augmented Reality, Extended reality, generative artificial intelligence, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Innovations in spatial computing and artificial intelligence (AI) are making it possible to overlay dynamic, interactive digital elements on the physical world. Soon, every object might have a real-time digital twin, enabling the “Internet of Things” so as to identify and interact with even unconnected items. This programmable reality would enable computational manipulation of the world around us through alteration of its appearance or functionality, similar to software, but for reality itself. Advances in AI language models have enabled zero-shot segmentation and understanding of the world, making it possible to query and manipulate objects with precision. However, this vision also demands natural and intuitive ways for humans to interact with these models through gestures, gaze, and existing devices. Augmented reality (AR) provides the ideal bridge between AI output and human input in the physical world. Moreover, diffusion models and physics simulations offer exciting possibilities for content generation and editing, allowing us to transform everyday activities into extraordinary experiences. As AR devices become ubiquitous and indistinguishable from reality, these technologies blur the lines between reality and simulations. This raises profound questions about how we perceive and experience the world while having implications for memory, learning, and even behavior. Programmable reality enabled by AR and AI has vast potential to reshape our relationships with the digital realm, ultimately making it an extension of the physical realm. © 2025 Elsevier B.V., All rights reserved.
Vadisetty, R.; Polamarasetti, A.; Goyal, M. K.; Rongali, S. K.; Prajapati, S. K.; Butani, J. B.
Cloud-Based Immersive Learning: The Role of Virtual Reality, Big Data, and Generative AI in Transformative Education Experiences Proceedings Article
In: Mishra, S.; Tripathy, H. K.; Mohanty, J. R. (Ed.): Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331523022 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Big Data, Cloud analytics, Cloud environments, Cloud-based, Cloud-based learning, E-Learning, Engineering education, Generative AI, generative artificial intelligence, Immersive learning, Learning analytic, learning analytics, Learning systems, Metadata, Personalized Education, Personalized learning, Real time analysis, Realistic simulation, Virtual environments, Virtual Reality
@inproceedings{vadisetty_cloud-based_2025,
title = {Cloud-Based Immersive Learning: The Role of Virtual Reality, Big Data, and Generative AI in Transformative Education Experiences},
author = {R. Vadisetty and A. Polamarasetti and M. K. Goyal and S. K. Rongali and S. K. Prajapati and J. B. Butani},
editor = {S. Mishra and H. K. Tripathy and J. R. Mohanty},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018048438&doi=10.1109%2FASSIC64892.2025.11158636&partnerID=40&md5=6d832a0f4460d2eb93e357faba143a32},
doi = {10.1109/ASSIC64892.2025.11158636},
isbn = {9798331523022 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Immersive learning transforms education by integrating Virtual Reality (VR), Big Data, and Generative Artificial Intelligence (AI) in cloud environments. This work discusses these technologies' contribution towards increased engagement, personalized learning, and recall through flexible and interactive experiences. Realistic simulations in a secure environment, real-time analysis via Big Data, and dynamically personalized information via Generative AI make immersive learning a reality. Nevertheless, scalability, security, and ease of integration are yet to be addressed. This article proposes an integrated model for cloud-based immersive learning, comparing conventional and AI-facilitated approaches through experimental evaluation. Besides, technical, ethical, and legislative considerations and future directions for inquiry are addressed. In conclusion, with its potential for personalized, scalable, and data-intensive instruction, AI-facilitated immersive learning is a transformational technology for educational delivery. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Artificial intelligence, Big Data, Cloud analytics, Cloud environments, Cloud-based, Cloud-based learning, E-Learning, Engineering education, Generative AI, generative artificial intelligence, Immersive learning, Learning analytic, learning analytics, Learning systems, Metadata, Personalized Education, Personalized learning, Real time analysis, Realistic simulation, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Immersive learning transforms education by integrating Virtual Reality (VR), Big Data, and Generative Artificial Intelligence (AI) in cloud environments. This work discusses these technologies' contribution towards increased engagement, personalized learning, and recall through flexible and interactive experiences. Realistic simulations in a secure environment, real-time analysis via Big Data, and dynamically personalized information via Generative AI make immersive learning a reality. Nevertheless, scalability, security, and ease of integration are yet to be addressed. This article proposes an integrated model for cloud-based immersive learning, comparing conventional and AI-facilitated approaches through experimental evaluation. Besides, technical, ethical, and legislative considerations and future directions for inquiry are addressed. In conclusion, with its potential for personalized, scalable, and data-intensive instruction, AI-facilitated immersive learning is a transformational technology for educational delivery. © 2025 Elsevier B.V., All rights reserved.
Vadisetty, R.; Polamarasetti, A.; Goyal, M. K.; Rongali, S. K.; Prajapati, S. K.; Butani, J. B.
Generative AI for Creating Immersive Learning Environments: Virtual Reality and Beyond Proceedings Article
In: Mishra, S.; Tripathy, H. K.; Mohanty, J. R. (Ed.): Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331523022 (ISBN).
Abstract | Links | BibTeX | Tags: AI in Education, Artificial intelligence in education, Augmented Reality, Augmented Reality (AR), Computer aided instruction, E-Learning, Educational spaces, Generative adversarial networks, Generative AI, generative artificial intelligence, Immersive, Immersive learning, Learning Environments, Learning systems, Personalized learning, Virtual and augmented reality, Virtual environments, Virtual Reality, Virtual Reality (VR)
@inproceedings{vadisetty_generative_2025,
title = {Generative AI for Creating Immersive Learning Environments: Virtual Reality and Beyond},
author = {R. Vadisetty and A. Polamarasetti and M. K. Goyal and S. K. Rongali and S. K. Prajapati and J. B. Butani},
editor = {S. Mishra and H. K. Tripathy and J. R. Mohanty},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018128093&doi=10.1109%2FASSIC64892.2025.11158626&partnerID=40&md5=b29a005f42262bf50c58d7708e2ed91a},
doi = {10.1109/ASSIC64892.2025.11158626},
isbn = {9798331523022 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Generative Artificial Intelligence (AI) revolutionizes immersive educational spaces with dynamic, personalized, and interactive experiences. In this article, Generative AI addresses its role in Virtual and Augmented Realities through automated creation, personalized learning pathways, and heightened engagement. With Generative AI, educational simulations can adapt to learner performance, produce interactive characters, and present real-time feedback through models such as Generative Adversarial Networks (GANs) and Transformerbased AI. Considering its potential, computational limitations, ethics, and authentic content concerns must be considered. In its examination, current implementations, benefits, and impediments, such as AI-powered flexible learning, are discussed in detail in this work. In conclusion, Generative AI's role in changing immersive instruction and opening doors for amplified and augmented educational offerings is stressed. © 2025 Elsevier B.V., All rights reserved.},
keywords = {AI in Education, Artificial intelligence in education, Augmented Reality, Augmented Reality (AR), Computer aided instruction, E-Learning, Educational spaces, Generative adversarial networks, Generative AI, generative artificial intelligence, Immersive, Immersive learning, Learning Environments, Learning systems, Personalized learning, Virtual and augmented reality, Virtual environments, Virtual Reality, Virtual Reality (VR)},
pubstate = {published},
tppubtype = {inproceedings}
}
Generative Artificial Intelligence (AI) revolutionizes immersive educational spaces with dynamic, personalized, and interactive experiences. In this article, Generative AI addresses its role in Virtual and Augmented Realities through automated creation, personalized learning pathways, and heightened engagement. With Generative AI, educational simulations can adapt to learner performance, produce interactive characters, and present real-time feedback through models such as Generative Adversarial Networks (GANs) and Transformerbased AI. Considering its potential, computational limitations, ethics, and authentic content concerns must be considered. In its examination, current implementations, benefits, and impediments, such as AI-powered flexible learning, are discussed in detail in this work. In conclusion, Generative AI's role in changing immersive instruction and opening doors for amplified and augmented educational offerings is stressed. © 2025 Elsevier B.V., All rights reserved.
Wei, X.; Wang, L.; Lee, L. -K.; Liu, R.
Multiple Generative AI Pedagogical Agents in Augmented Reality Environments: A Study on Implementing the 5E Model in Science Education Journal Article
In: Journal of Educational Computing Research, vol. 63, no. 2, pp. 336–371, 2025, ISSN: 07356331 (ISSN); 15414140 (ISSN), (Publisher: SAGE Publications Inc.).
Abstract | Links | BibTeX | Tags: 5E learning model, Augmented Reality, elementary science education, generative artificial intelligence, Pedagogical agents
@article{wei_multiple_2025,
title = {Multiple Generative AI Pedagogical Agents in Augmented Reality Environments: A Study on Implementing the 5E Model in Science Education},
author = {X. Wei and L. Wang and L. -K. Lee and R. Liu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211165915&doi=10.1177%2F07356331241305519&partnerID=40&md5=375969e49e645079e367da02a8c61a21},
doi = {10.1177/07356331241305519},
issn = {07356331 (ISSN); 15414140 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Journal of Educational Computing Research},
volume = {63},
number = {2},
pages = {336–371},
abstract = {Notwithstanding the growing advantages of incorporating Augmented Reality (AR) in science education, the pedagogical use of AR combined with Pedagogical Agents (PAs) remains underexplored. Additionally, few studies have examined the integration of Generative Artificial Intelligence (GAI) into science education to create GAI-enhanced PAs (GPAs) that enrich the learning experiences. To address these gaps, this study designed and implemented a GPA-enhanced 5E model within AR environments to scaffold students’ science learning. A mixed-methods design was conducted to investigate the effectiveness of the proposed approach on students’ academic achievement, cognitive load, and their perceptions of GPAs as learning aids through using the 5E model. Sixty sixth-grade students from two complete classes were randomly assigned to either an experimental group engaged in AR science learning with a GPA-enhanced 5E approach or a control group that followed the traditional 5E method. The findings revealed that the GPA-enhanced 5E approach in AR environments significantly improved students’ academic achievement and decreased cognitive load. Furthermore, students in the experimental group reported positive perceptions of the GPA-enhanced 5E method during the AR science lessons. The findings offer valuable insights for instructional designers and educators who leverage advanced educational technologies to support science learning aligned with constructivist principles. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: SAGE Publications Inc.},
keywords = {5E learning model, Augmented Reality, elementary science education, generative artificial intelligence, Pedagogical agents},
pubstate = {published},
tppubtype = {article}
}
Notwithstanding the growing advantages of incorporating Augmented Reality (AR) in science education, the pedagogical use of AR combined with Pedagogical Agents (PAs) remains underexplored. Additionally, few studies have examined the integration of Generative Artificial Intelligence (GAI) into science education to create GAI-enhanced PAs (GPAs) that enrich the learning experiences. To address these gaps, this study designed and implemented a GPA-enhanced 5E model within AR environments to scaffold students’ science learning. A mixed-methods design was conducted to investigate the effectiveness of the proposed approach on students’ academic achievement, cognitive load, and their perceptions of GPAs as learning aids through using the 5E model. Sixty sixth-grade students from two complete classes were randomly assigned to either an experimental group engaged in AR science learning with a GPA-enhanced 5E approach or a control group that followed the traditional 5E method. The findings revealed that the GPA-enhanced 5E approach in AR environments significantly improved students’ academic achievement and decreased cognitive load. Furthermore, students in the experimental group reported positive perceptions of the GPA-enhanced 5E method during the AR science lessons. The findings offer valuable insights for instructional designers and educators who leverage advanced educational technologies to support science learning aligned with constructivist principles. © 2025 Elsevier B.V., All rights reserved.
Shi, J.; Jain, R.; Chi, S.; Doh, H.; Chi, H. -G.; Quinn, A. J.; Ramani, K.
CARING-AI: Towards Authoring Context-aware Augmented Reality INstruction through Generative Artificial Intelligence Proceedings Article
In: Conf Hum Fact Comput Syst Proc, Association for Computing Machinery, 2025, ISBN: 9798400713958 (ISBN); 9798400713941 (ISBN).
Abstract | Links | BibTeX | Tags: 'current, Application scenario, AR application, Augmented Reality, Context-Aware, Contextual information, Generative adversarial networks, generative artificial intelligence, Humanoid avatars, In-situ learning, Learning experiences, Power
@inproceedings{shi_caring-ai_2025,
title = {CARING-AI: Towards Authoring Context-aware Augmented Reality INstruction through Generative Artificial Intelligence},
author = {J. Shi and R. Jain and S. Chi and H. Doh and H. -G. Chi and A. J. Quinn and K. Ramani},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005725461&doi=10.1145%2F3706598.3713348&partnerID=40&md5=ffda868f61789e926364753a2be4c168},
doi = {10.1145/3706598.3713348},
isbn = {9798400713958 (ISBN); 9798400713941 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf Hum Fact Comput Syst Proc},
publisher = {Association for Computing Machinery},
abstract = {Context-aware AR instruction enables adaptive and in-situ learning experiences. However, hardware limitations and expertise requirements constrain the creation of such instructions. With recent developments in Generative Artificial Intelligence (Gen-AI), current research tries to tackle these constraints by deploying AI-generated content (AIGC) in AR applications. However, our preliminary study with six AR practitioners revealed that the current AIGC lacks contextual information to adapt to varying application scenarios and is therefore limited in authoring. To utilize the strong generative power of GenAI to ease the authoring of AR instruction while capturing the context, we developed CARING-AI, an AR system to author context-aware humanoid-avatar-based instructions with GenAI. By navigating in the environment, users naturally provide contextual information to generate humanoid-avatar animation as AR instructions that blend in the context spatially and temporally. We showcased three application scenarios of CARING-AI: Asynchronous Instructions, Remote Instructions, and Ad Hoc Instructions based on a design space of AIGC in AR Instructions. With two user studies (N=12), we assessed the system usability of CARING-AI and demonstrated the easiness and effectiveness of authoring with Gen-AI. © 2025 Elsevier B.V., All rights reserved.},
keywords = {'current, Application scenario, AR application, Augmented Reality, Context-Aware, Contextual information, Generative adversarial networks, generative artificial intelligence, Humanoid avatars, In-situ learning, Learning experiences, Power},
pubstate = {published},
tppubtype = {inproceedings}
}
Context-aware AR instruction enables adaptive and in-situ learning experiences. However, hardware limitations and expertise requirements constrain the creation of such instructions. With recent developments in Generative Artificial Intelligence (Gen-AI), current research tries to tackle these constraints by deploying AI-generated content (AIGC) in AR applications. However, our preliminary study with six AR practitioners revealed that the current AIGC lacks contextual information to adapt to varying application scenarios and is therefore limited in authoring. To utilize the strong generative power of GenAI to ease the authoring of AR instruction while capturing the context, we developed CARING-AI, an AR system to author context-aware humanoid-avatar-based instructions with GenAI. By navigating in the environment, users naturally provide contextual information to generate humanoid-avatar animation as AR instructions that blend in the context spatially and temporally. We showcased three application scenarios of CARING-AI: Asynchronous Instructions, Remote Instructions, and Ad Hoc Instructions based on a design space of AIGC in AR Instructions. With two user studies (N=12), we assessed the system usability of CARING-AI and demonstrated the easiness and effectiveness of authoring with Gen-AI. © 2025 Elsevier B.V., All rights reserved.
Suzuki, R.; González-Franco, M.; Sra, M.; Lindlbauer, D.
Everyday AR through AI-in-the-Loop Proceedings Article
In: Conf Hum Fact Comput Syst Proc, Association for Computing Machinery, 2025, ISBN: 9798400713958 (ISBN); 9798400713941 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Augmented reality content, Augmented reality hardware, Computer vision, Content creation, Context-Aware, Generative AI, generative artificial intelligence, Human-AI Interaction, Human-artificial intelligence interaction, Language Model, Large language model, large language models, machine learning, Machine-learning, Mixed reality, Virtual Reality, Virtualization
@inproceedings{suzuki_everyday_2025,
title = {Everyday AR through AI-in-the-Loop},
author = {R. Suzuki and M. González-Franco and M. Sra and D. Lindlbauer},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005752990&doi=10.1145%2F3706599.3706741&partnerID=40&md5=a5369bb371ce25feca340b4f5952e6a6},
doi = {10.1145/3706599.3706741},
isbn = {9798400713958 (ISBN); 9798400713941 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf Hum Fact Comput Syst Proc},
publisher = {Association for Computing Machinery},
abstract = {This workshop brings together experts and practitioners from augmented reality (AR) and artificial intelligence (AI) to shape the future of AI-in-the-loop everyday AR experiences. With recent advancements in both AR hardware and AI capabilities, we envision that everyday AR—always-available and seamlessly integrated into users’ daily environments—is becoming increasingly feasible. This workshop will explore how AI can drive such everyday AR experiences. We discuss a range of topics, including adaptive and context-aware AR, generative AR content creation, always-on AI assistants, AI-driven accessible design, and real-world-oriented AI agents. Our goal is to identify the opportunities and challenges in AI-enabled AR, focusing on creating novel AR experiences that seamlessly blend the digital and physical worlds. Through the workshop, we aim to foster collaboration, inspire future research, and build a community to advance the research field of AI-enhanced AR. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Augmented Reality, Augmented reality content, Augmented reality hardware, Computer vision, Content creation, Context-Aware, Generative AI, generative artificial intelligence, Human-AI Interaction, Human-artificial intelligence interaction, Language Model, Large language model, large language models, machine learning, Machine-learning, Mixed reality, Virtual Reality, Virtualization},
pubstate = {published},
tppubtype = {inproceedings}
}
This workshop brings together experts and practitioners from augmented reality (AR) and artificial intelligence (AI) to shape the future of AI-in-the-loop everyday AR experiences. With recent advancements in both AR hardware and AI capabilities, we envision that everyday AR—always-available and seamlessly integrated into users’ daily environments—is becoming increasingly feasible. This workshop will explore how AI can drive such everyday AR experiences. We discuss a range of topics, including adaptive and context-aware AR, generative AR content creation, always-on AI assistants, AI-driven accessible design, and real-world-oriented AI agents. Our goal is to identify the opportunities and challenges in AI-enabled AR, focusing on creating novel AR experiences that seamlessly blend the digital and physical worlds. Through the workshop, we aim to foster collaboration, inspire future research, and build a community to advance the research field of AI-enhanced AR. © 2025 Elsevier B.V., All rights reserved.
Abdelmagid, A. S.; Jabli, N. M.; Al-Mohaya, A. Y.; Teleb, A. A.
In: Sustainability (Switzerland), vol. 17, no. 12, 2025, ISSN: 20711050 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Abstract | Links | BibTeX | Tags: Artificial intelligence, digitization, e-entrepreneurship, entrepreneur, generative artificial intelligence, green digital economy, green economy, higher education, Learning, Metaverse, Sustainable development
@article{abdelmagid_integrating_2025,
title = {Integrating Interactive Metaverse Environments and Generative Artificial Intelligence to Promote the Green Digital Economy and e-Entrepreneurship in Higher Education},
author = {A. S. Abdelmagid and N. M. Jabli and A. Y. Al-Mohaya and A. A. Teleb},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105008981835&doi=10.3390%2Fsu17125594&partnerID=40&md5=acbdf61feea14fd78346f7022556bd03},
doi = {10.3390/su17125594},
issn = {20711050 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Sustainability (Switzerland)},
volume = {17},
number = {12},
abstract = {The rapid evolution of the Fourth Industrial Revolution has significantly transformed educational practices, necessitating the integration of advanced technologies into higher education to address contemporary sustainability challenges. This study explores the integration of interactive metaverse environments and generative artificial intelligence (GAI) in promoting the green digital economy and developing e-entrepreneurship skills among graduate students. Grounded in a quasi-experimental design, the research was conducted with a sample of 25 postgraduate students enrolled in the “Computers in Education” course at King Khalid University. A 3D immersive learning environment (FrameVR) was combined with GAI platforms (ChatGPT version 4.0, Elai.io version 2.5, Tome version 1.3) to create an innovative educational experience. Data were collected using validated instruments, including the Green Digital Economy Scale, the e-Entrepreneurship Scale, and a digital product evaluation rubric. The findings revealed statistically significant improvements in students’ awareness of green digital concepts, entrepreneurial competencies, and their ability to produce sustainable digital products. The study highlights the potential of immersive virtual learning environments and AI-driven content creation tools in enhancing digital literacy and sustainability-oriented innovation. It also underscores the urgent need to update educational strategies and curricula to prepare future professionals capable of navigating and shaping green digital economies. This research provides a practical and replicable model for universities seeking to embed sustainability through emerging technologies, supporting broader goals such as SDG 4 (Quality Education) and SDG 9 (Industry, Innovation, and Infrastructure). © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {Artificial intelligence, digitization, e-entrepreneurship, entrepreneur, generative artificial intelligence, green digital economy, green economy, higher education, Learning, Metaverse, Sustainable development},
pubstate = {published},
tppubtype = {article}
}
The rapid evolution of the Fourth Industrial Revolution has significantly transformed educational practices, necessitating the integration of advanced technologies into higher education to address contemporary sustainability challenges. This study explores the integration of interactive metaverse environments and generative artificial intelligence (GAI) in promoting the green digital economy and developing e-entrepreneurship skills among graduate students. Grounded in a quasi-experimental design, the research was conducted with a sample of 25 postgraduate students enrolled in the “Computers in Education” course at King Khalid University. A 3D immersive learning environment (FrameVR) was combined with GAI platforms (ChatGPT version 4.0, Elai.io version 2.5, Tome version 1.3) to create an innovative educational experience. Data were collected using validated instruments, including the Green Digital Economy Scale, the e-Entrepreneurship Scale, and a digital product evaluation rubric. The findings revealed statistically significant improvements in students’ awareness of green digital concepts, entrepreneurial competencies, and their ability to produce sustainable digital products. The study highlights the potential of immersive virtual learning environments and AI-driven content creation tools in enhancing digital literacy and sustainability-oriented innovation. It also underscores the urgent need to update educational strategies and curricula to prepare future professionals capable of navigating and shaping green digital economies. This research provides a practical and replicable model for universities seeking to embed sustainability through emerging technologies, supporting broader goals such as SDG 4 (Quality Education) and SDG 9 (Industry, Innovation, and Infrastructure). © 2025 Elsevier B.V., All rights reserved.
Oh, S.; Jung, M.; Kim, T.
EnvMat: A Network for Simultaneous Generation of PBR Maps and Environment Maps from a Single Image Journal Article
In: Electronics (Switzerland), vol. 14, no. 13, 2025, ISSN: 20799292 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Abstract | Links | BibTeX | Tags: 3D graphics, Auto encoders, Cameras, Diffusion, Diffusion Model, Environment maps, generative artificial intelligence, Image understanding, Latent diffusion model, latent diffusion models, Metaverse, Metaverses, Neural Networks, Physically based rendering, physically based rendering (PBR), Rendering (computer graphics), Tellurium compounds, Three dimensional computer graphics, Variational Autoencoder, Variational Autoencoders (VAEs), Variational techniques, Virtual Reality, Visualization
@article{oh_envmat_2025,
title = {EnvMat: A Network for Simultaneous Generation of PBR Maps and Environment Maps from a Single Image},
author = {S. Oh and M. Jung and T. Kim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105010306182&doi=10.3390%2Felectronics14132554&partnerID=40&md5=a6e24d71cb6f1e632ee2415b99f68c0e},
doi = {10.3390/electronics14132554},
issn = {20799292 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Electronics (Switzerland)},
volume = {14},
number = {13},
abstract = {Generative neural networks have expanded from text and image generation to creating realistic 3D graphics, which are critical for immersive virtual environments. Physically Based Rendering (PBR)—crucial for realistic 3D graphics—depends on PBR maps, environment (env) maps for lighting, and camera viewpoints. Current research mainly generates PBR maps separately, often using fixed env maps and camera poses. This limitation reduces visual consistency and immersion in 3D spaces. Addressing this, we propose EnvMat, a diffusion-based model that simultaneously generates PBR and env maps. EnvMat uses two Variational Autoencoders (VAEs) for map reconstruction and a Latent Diffusion UNet. Experimental results show that EnvMat surpasses the existing methods in preserving visual accuracy, as validated through metrics like L-PIPS, MS-SSIM, and CIEDE2000. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {3D graphics, Auto encoders, Cameras, Diffusion, Diffusion Model, Environment maps, generative artificial intelligence, Image understanding, Latent diffusion model, latent diffusion models, Metaverse, Metaverses, Neural Networks, Physically based rendering, physically based rendering (PBR), Rendering (computer graphics), Tellurium compounds, Three dimensional computer graphics, Variational Autoencoder, Variational Autoencoders (VAEs), Variational techniques, Virtual Reality, Visualization},
pubstate = {published},
tppubtype = {article}
}
Generative neural networks have expanded from text and image generation to creating realistic 3D graphics, which are critical for immersive virtual environments. Physically Based Rendering (PBR)—crucial for realistic 3D graphics—depends on PBR maps, environment (env) maps for lighting, and camera viewpoints. Current research mainly generates PBR maps separately, often using fixed env maps and camera poses. This limitation reduces visual consistency and immersion in 3D spaces. Addressing this, we propose EnvMat, a diffusion-based model that simultaneously generates PBR and env maps. EnvMat uses two Variational Autoencoders (VAEs) for map reconstruction and a Latent Diffusion UNet. Experimental results show that EnvMat surpasses the existing methods in preserving visual accuracy, as validated through metrics like L-PIPS, MS-SSIM, and CIEDE2000. © 2025 Elsevier B.V., All rights reserved.
Dang, B.; Huynh, L.; Gul, F.; Rosé, C.; Järvelä, S.; Nguyen, A.
Human–AI collaborative learning in mixed reality: Examining the cognitive and socio-emotional interactions Journal Article
In: British Journal of Educational Technology, vol. 56, no. 5, pp. 2078–2101, 2025, ISSN: 00071013 (ISSN); 14678535 (ISSN), (Publisher: John Wiley and Sons Inc).
Abstract | Links | BibTeX | Tags: Artificial intelligence agent, Collaborative learning, Educational robots, Embodied agent, Emotional intelligence, Emotional interactions, Generative adversarial networks, generative artificial intelligence, Hierarchical clustering, Human–AI collaboration, Interaction pattern, Mixed reality, ordered network analysis, Ordered network analyze, Social behavior, Social interactions, Social psychology, Students, Supervised learning, Teaching
@article{dang_humanai_2025,
title = {Human–AI collaborative learning in mixed reality: Examining the cognitive and socio-emotional interactions},
author = {B. Dang and L. Huynh and F. Gul and C. Rosé and S. Järvelä and A. Nguyen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007896240&doi=10.1111%2Fbjet.13607&partnerID=40&md5=1c80a5bfe5917e7a9b14ee5809da232f},
doi = {10.1111/bjet.13607},
issn = {00071013 (ISSN); 14678535 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {British Journal of Educational Technology},
volume = {56},
number = {5},
pages = {2078–2101},
abstract = {The rise of generative artificial intelligence (GAI), especially with multimodal large language models like GPT-4o, sparked transformative potential and challenges for learning and teaching. With potential as a cognitive offloading tool, GAI can enable learners to focus on higher-order thinking and creativity. Yet, this also raises questions about integration into traditional education due to the limited research on learners' interactions with GAI. Some studies with GAI focus on text-based human–AI interactions, while research on embodied GAI in immersive environments like mixed reality (MR) remains unexplored. To address this, this study investigates interaction dynamics between learners and embodied GAI agents in MR, examining cognitive and socio-emotional interactions during collaborative learning. We investigated the paired interactive patterns between a student and an embodied GAI agent in MR, based on data from 26 higher education students with 1317 recorded activities. Data were analysed using a multi-layered learning analytics approach, including quantitative content analysis, sequence analysis via hierarchical clustering and pattern analysis through ordered network analysis (ONA). Our findings identified two interaction patterns: type (1) AI-led Supported Exploratory Questioning (AISQ) and type (2) Learner-Initiated Inquiry (LII) group. Despite their distinction in characteristic, both types demonstrated comparable levels of socio-emotional engagement and exhibited meaningful cognitive engagement, surpassing the superficial content reproduction that can be observed in interactions with GPT models. This study contributes to the human–AI collaboration and learning studies, extending understanding to learning in MR environments and highlighting implications for designing AI-based educational tools. Practitioner notes What is already known about this topic Socio-emotional interactions are fundamental to cognitive processes and play a critical role in collaborative learning. Generative artificial intelligence (GAI) holds transformative potential for education but raises questions about how learners interact with such technology. Most existing research focuses on text-based interactions with GAI; there is limited empirical evidence on how embodied GAI agents within immersive environments like Mixed Reality (MR) influence the cognitive and socio-emotional interactions for learning and regulation. What this paper adds Provides first empirical insights into cognitive and socio-emotional interaction patterns between learners and embodied GAI agents in MR environments. Identifies two distinct interaction patterns: AISQ type (structured, guided, supportive) and LII type (inquiry-driven, exploratory, engaging), demonstrating how these patterns influence collaborative learning dynamics. Shows that both interaction types facilitate meaningful cognitive engagement, moving beyond superficial content reproduction commonly associated with GAI interactions. Implications for practice and/or policy Insights from the identified interaction patterns can inform the design of teaching strategies that effectively integrate embodied GAI agents to enhance both cognitive and socio-emotional engagement. Findings can guide the development of AI-based educational tools that capitalise on the capabilities of embodied GAI agents, supporting a balance between structured guidance and exploratory learning. Highlights the need for ethical considerations in adopting embodied GAI agents, particularly regarding the human-like realism of these agents and potential impacts on learner dependency and interaction norms. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: John Wiley and Sons Inc},
keywords = {Artificial intelligence agent, Collaborative learning, Educational robots, Embodied agent, Emotional intelligence, Emotional interactions, Generative adversarial networks, generative artificial intelligence, Hierarchical clustering, Human–AI collaboration, Interaction pattern, Mixed reality, ordered network analysis, Ordered network analyze, Social behavior, Social interactions, Social psychology, Students, Supervised learning, Teaching},
pubstate = {published},
tppubtype = {article}
}
The rise of generative artificial intelligence (GAI), especially with multimodal large language models like GPT-4o, sparked transformative potential and challenges for learning and teaching. With potential as a cognitive offloading tool, GAI can enable learners to focus on higher-order thinking and creativity. Yet, this also raises questions about integration into traditional education due to the limited research on learners' interactions with GAI. Some studies with GAI focus on text-based human–AI interactions, while research on embodied GAI in immersive environments like mixed reality (MR) remains unexplored. To address this, this study investigates interaction dynamics between learners and embodied GAI agents in MR, examining cognitive and socio-emotional interactions during collaborative learning. We investigated the paired interactive patterns between a student and an embodied GAI agent in MR, based on data from 26 higher education students with 1317 recorded activities. Data were analysed using a multi-layered learning analytics approach, including quantitative content analysis, sequence analysis via hierarchical clustering and pattern analysis through ordered network analysis (ONA). Our findings identified two interaction patterns: type (1) AI-led Supported Exploratory Questioning (AISQ) and type (2) Learner-Initiated Inquiry (LII) group. Despite their distinction in characteristic, both types demonstrated comparable levels of socio-emotional engagement and exhibited meaningful cognitive engagement, surpassing the superficial content reproduction that can be observed in interactions with GPT models. This study contributes to the human–AI collaboration and learning studies, extending understanding to learning in MR environments and highlighting implications for designing AI-based educational tools. Practitioner notes What is already known about this topic Socio-emotional interactions are fundamental to cognitive processes and play a critical role in collaborative learning. Generative artificial intelligence (GAI) holds transformative potential for education but raises questions about how learners interact with such technology. Most existing research focuses on text-based interactions with GAI; there is limited empirical evidence on how embodied GAI agents within immersive environments like Mixed Reality (MR) influence the cognitive and socio-emotional interactions for learning and regulation. What this paper adds Provides first empirical insights into cognitive and socio-emotional interaction patterns between learners and embodied GAI agents in MR environments. Identifies two distinct interaction patterns: AISQ type (structured, guided, supportive) and LII type (inquiry-driven, exploratory, engaging), demonstrating how these patterns influence collaborative learning dynamics. Shows that both interaction types facilitate meaningful cognitive engagement, moving beyond superficial content reproduction commonly associated with GAI interactions. Implications for practice and/or policy Insights from the identified interaction patterns can inform the design of teaching strategies that effectively integrate embodied GAI agents to enhance both cognitive and socio-emotional engagement. Findings can guide the development of AI-based educational tools that capitalise on the capabilities of embodied GAI agents, supporting a balance between structured guidance and exploratory learning. Highlights the need for ethical considerations in adopting embodied GAI agents, particularly regarding the human-like realism of these agents and potential impacts on learner dependency and interaction norms. © 2025 Elsevier B.V., All rights reserved.
2024
Greca, A. D.; Amaro, I.; Barra, P.; Rosapepe, E.; Tortora, G.
Enhancing therapeutic engagement in Mental Health through Virtual Reality and Generative AI: A co-creation approach to trust building Proceedings Article
In: M., Cannataro; H., Zheng; L., Gao; J., Cheng; J.L., Miranda; E., Zumpano; X., Hu; Y.-R., Cho; T., Park (Ed.): Proc. - IEEE Int. Conf. Bioinform. Biomed., BIBM, pp. 6805–6811, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835038622-6 (ISBN).
Abstract | Links | BibTeX | Tags: Co-creation, Electronic health record, Fundamental component, Generative adversarial networks, Generative AI, generative artificial intelligence, Immersive, Mental health, Personalized therapies, Personalized Therapy, Three-dimensional object, Trust, Trust building, Virtual environments, Virtual Reality, Virtual Reality (VR)
@inproceedings{greca_enhancing_2024,
title = {Enhancing therapeutic engagement in Mental Health through Virtual Reality and Generative AI: A co-creation approach to trust building},
author = {A. D. Greca and I. Amaro and P. Barra and E. Rosapepe and G. Tortora},
editor = {Cannataro M. and Zheng H. and Gao L. and Cheng J. and Miranda J.L. and Zumpano E. and Hu X. and Cho Y.-R. and Park T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217278235&doi=10.1109%2fBIBM62325.2024.10822177&partnerID=40&md5=ed42f7ca6a0e52e9945402e2c439a7f0},
doi = {10.1109/BIBM62325.2024.10822177},
isbn = {979-835038622-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Bioinform. Biomed., BIBM},
pages = {6805–6811},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Trust is a fundamental component of effective therapeutic relationships, significantly influencing patient engagement and treatment outcomes in mental health care. This paper presents a preliminary study aimed at enhancing trust through the co-creation of virtual therapeutic environments using generative artificial intelligence (AI). We propose a multimodal AI model, integrated into a virtual reality (VR) platform developed in Unity, which generates three-dimensional (3D) objects from textual descriptions. This approach allows patients to actively participate in shaping their therapeutic environment, fostering a collaborative atmosphere that enhances trust between patients and therapists. The methodology is structured into four phases, combining non-immersive and immersive experiences to co-create personalized therapeutic spaces and 3D objects symbolizing emotional or psychological states. Preliminary results demonstrate the system's potential in improving the therapeutic process through the real-time creation of virtual objects that reflect patient needs, with high-quality mesh generation and semantic coherence. This work offers new possibilities for patient-centered care in mental health services, suggesting that virtual co-creation can improve therapeutic efficacy by promoting trust and emotional engagement. © 2024 IEEE.},
keywords = {Co-creation, Electronic health record, Fundamental component, Generative adversarial networks, Generative AI, generative artificial intelligence, Immersive, Mental health, Personalized therapies, Personalized Therapy, Three-dimensional object, Trust, Trust building, Virtual environments, Virtual Reality, Virtual Reality (VR)},
pubstate = {published},
tppubtype = {inproceedings}
}
Trust is a fundamental component of effective therapeutic relationships, significantly influencing patient engagement and treatment outcomes in mental health care. This paper presents a preliminary study aimed at enhancing trust through the co-creation of virtual therapeutic environments using generative artificial intelligence (AI). We propose a multimodal AI model, integrated into a virtual reality (VR) platform developed in Unity, which generates three-dimensional (3D) objects from textual descriptions. This approach allows patients to actively participate in shaping their therapeutic environment, fostering a collaborative atmosphere that enhances trust between patients and therapists. The methodology is structured into four phases, combining non-immersive and immersive experiences to co-create personalized therapeutic spaces and 3D objects symbolizing emotional or psychological states. Preliminary results demonstrate the system's potential in improving the therapeutic process through the real-time creation of virtual objects that reflect patient needs, with high-quality mesh generation and semantic coherence. This work offers new possibilities for patient-centered care in mental health services, suggesting that virtual co-creation can improve therapeutic efficacy by promoting trust and emotional engagement. © 2024 IEEE.
Cuervo-Rosillo, R.; Zarraonandia, T.; Díaz, P.
Using Generative AI to Support Non-Experts in the Creation of Immersive Experiences Proceedings Article
In: ACM Int. Conf. Proc. Ser., Association for Computing Machinery, 2024, ISBN: 979-840071764-2 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, End-Users, generative artificial intelligence, Immersive, immersive experience, Immersive Experiences, Natural languages, Speech commands, User interfaces, Virtual Reality
@inproceedings{cuervo-rosillo_using_2024,
title = {Using Generative AI to Support Non-Experts in the Creation of Immersive Experiences},
author = {R. Cuervo-Rosillo and T. Zarraonandia and P. Díaz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195422750&doi=10.1145%2f3656650.3656733&partnerID=40&md5=00d53df1d6b30acc6d281bb86ead73ab},
doi = {10.1145/3656650.3656733},
isbn = {979-840071764-2 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {ACM Int. Conf. Proc. Ser.},
publisher = {Association for Computing Machinery},
abstract = {This work focuses on exploring the use of Generative Artificial Intelligence to assist end-users in creating immersive experiences. We present a prototype that supports the creation and edition of virtual environments using speech commands expressed in natural language. © 2024 Owner/Author.},
keywords = {Artificial intelligence, End-Users, generative artificial intelligence, Immersive, immersive experience, Immersive Experiences, Natural languages, Speech commands, User interfaces, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This work focuses on exploring the use of Generative Artificial Intelligence to assist end-users in creating immersive experiences. We present a prototype that supports the creation and edition of virtual environments using speech commands expressed in natural language. © 2024 Owner/Author.
Armony, Y.; Hazzan, O.
Springer Nature, 2024, ISBN: 978-303172790-0 (ISBN); 978-303172789-4 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence in education, Augmented Reality, Characteristics of technology, Educational reform, generative artificial intelligence, Inevitable technology, Virtual Reality
@book{armony_inevitability_2024,
title = {Inevitability of AI technology in education: Futurism perspectives for education for the next two decades},
author = {Y. Armony and O. Hazzan},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001750559&doi=10.1007%2f978-3-031-72790-0&partnerID=40&md5=7c7d979b5d48a1abd8830708a5b27d89},
doi = {10.1007/978-3-031-72790-0},
isbn = {978-303172790-0 (ISBN); 978-303172789-4 (ISBN)},
year = {2024},
date = {2024-01-01},
publisher = {Springer Nature},
series = {Inevitability of AI Technology in Education: Futurism Perspectives for Education for the Next Two Decades},
abstract = {This book layouts historic and future perspectives at the introduction of technology into education systems: On the one hand, the book attempts to explain why despite numerous attempts, technology has struggled to integrate successfully into the education system for over a century; on the other hand, it explores whether this trend will persist in the foreseeable future, questioning if emerging technologies, like virtual reality or Gen-AI will ever be embraced by education systems worldwide, and introducing a hypothesis that these technologies will become inevitable so that education systems will have a little choice in adopting them. The underlying perspective is that education systems need to prepare for this new future and better start doing so now. The book encompasses three key areas: education, technology, and future studies, with a focus on how technology will shape the future of education. It begins by examining past failures of integrating technology into education, analyzing the reasons behind these setbacks. It progresses to assess the potential integration of future technologies (10-20 years from now), exploring a feasible scenario and the force implications on learning, teachers, and the system. Examining recent attempts to implement technology in education reveals numerous reasons for failure. A significant contributing factor appears to be inherent conflicts within the education system's fundamental structure. These conflicts, involving goals, curricula, organizational structure, pedagogy, and student management, prevent the system from embracing reforms or new technologies. Envisioning a future where technology will deeply 'know' the students, 'sense' their environment, 'understand' the context and the situation, 'explain' and 'advise' them on the best suitable behavior or activity, the book anticipates applications in education ranging from ensuring personal safety and health to enhancing knowledge acquisition and decision-making. As the book explores the potential inevitability of technology in education, it recognizes the transformative impact on teachers and students and outlines possible desire scenario to aid in preparation, such as, personalized education to better suit student's capabilities, needs, and desires; how to motivate students to learn in an environment where all tasks can be done by machines; ethical issues; the new role of the school, the educator, and the system, etc. This book is especially suitable for teachers, educators, public officials, and anyone interested in the future of education. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. All rights reserved.},
keywords = {Artificial intelligence in education, Augmented Reality, Characteristics of technology, Educational reform, generative artificial intelligence, Inevitable technology, Virtual Reality},
pubstate = {published},
tppubtype = {book}
}
This book layouts historic and future perspectives at the introduction of technology into education systems: On the one hand, the book attempts to explain why despite numerous attempts, technology has struggled to integrate successfully into the education system for over a century; on the other hand, it explores whether this trend will persist in the foreseeable future, questioning if emerging technologies, like virtual reality or Gen-AI will ever be embraced by education systems worldwide, and introducing a hypothesis that these technologies will become inevitable so that education systems will have a little choice in adopting them. The underlying perspective is that education systems need to prepare for this new future and better start doing so now. The book encompasses three key areas: education, technology, and future studies, with a focus on how technology will shape the future of education. It begins by examining past failures of integrating technology into education, analyzing the reasons behind these setbacks. It progresses to assess the potential integration of future technologies (10-20 years from now), exploring a feasible scenario and the force implications on learning, teachers, and the system. Examining recent attempts to implement technology in education reveals numerous reasons for failure. A significant contributing factor appears to be inherent conflicts within the education system's fundamental structure. These conflicts, involving goals, curricula, organizational structure, pedagogy, and student management, prevent the system from embracing reforms or new technologies. Envisioning a future where technology will deeply 'know' the students, 'sense' their environment, 'understand' the context and the situation, 'explain' and 'advise' them on the best suitable behavior or activity, the book anticipates applications in education ranging from ensuring personal safety and health to enhancing knowledge acquisition and decision-making. As the book explores the potential inevitability of technology in education, it recognizes the transformative impact on teachers and students and outlines possible desire scenario to aid in preparation, such as, personalized education to better suit student's capabilities, needs, and desires; how to motivate students to learn in an environment where all tasks can be done by machines; ethical issues; the new role of the school, the educator, and the system, etc. This book is especially suitable for teachers, educators, public officials, and anyone interested in the future of education. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. All rights reserved.