AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
How to
Here you can find the complete list of our publications.
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.
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
Mekki, Y. M.; Simon, L. V.; Freeman, W. D.; Qadir, J.
Medical Education Metaverses (MedEd Metaverses): Opportunities, Use Case, and Guidelines Journal Article
In: Computer, vol. 58, no. 3, pp. 60–70, 2025, ISSN: 00189162 (ISSN).
Abstract | Links | BibTeX | Tags: Adaptive feedback, Augmented Reality, Immersive learning, Medical education, Metaverses, Performance tracking, Remote resources, Remote training, Resource efficiencies, Training efficiency, Virtual environments
@article{mekki_medical_2025,
title = {Medical Education Metaverses (MedEd Metaverses): Opportunities, Use Case, and Guidelines},
author = {Y. M. Mekki and L. V. Simon and W. D. Freeman and J. Qadir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218631349&doi=10.1109%2fMC.2024.3474033&partnerID=40&md5=65f46cf9b8d98eaf0fcd6843b9ebc41e},
doi = {10.1109/MC.2024.3474033},
issn = {00189162 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Computer},
volume = {58},
number = {3},
pages = {60–70},
abstract = {This article explores how artificial intelligence (AI), particularly generative AI (GenAI), can enhance extended reality (XR) applications in medical education (MedEd) metaverses. We compare traditional augmented reality/virtual reality methods with AI-enabled XR metaverses, highlighting improvements in immersive learning, adaptive feedback, personalized performance tracking, remote training, and resource efficiency. © 1970-2012 IEEE.},
keywords = {Adaptive feedback, Augmented Reality, Immersive learning, Medical education, Metaverses, Performance tracking, Remote resources, Remote training, Resource efficiencies, Training efficiency, Virtual environments},
pubstate = {published},
tppubtype = {article}
}
This article explores how artificial intelligence (AI), particularly generative AI (GenAI), can enhance extended reality (XR) applications in medical education (MedEd) metaverses. We compare traditional augmented reality/virtual reality methods with AI-enabled XR metaverses, highlighting improvements in immersive learning, adaptive feedback, personalized performance tracking, remote training, and resource efficiency. © 1970-2012 IEEE.
Linares-Pellicer, J.; Izquierdo-Domenech, J.; Ferri-Molla, I.; Aliaga-Torro, C.
Breaking the Bottleneck: Generative AI as the Solution for XR Content Creation in Education Book Section
In: Lecture Notes in Networks and Systems, vol. 1140, pp. 9–30, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 23673370 (ISSN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Augmented Reality, Breakings, Content creation, Contrastive Learning, Development process, Educational context, Federated learning, Generative adversarial networks, Immersive learning, Intelligence models, Learning experiences, Mixed reality, Resource intensity, Technical skills, Virtual environments
@incollection{linares-pellicer_breaking_2025,
title = {Breaking the Bottleneck: Generative AI as the Solution for XR Content Creation in Education},
author = {J. Linares-Pellicer and J. Izquierdo-Domenech and I. Ferri-Molla and C. Aliaga-Torro},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212478399&doi=10.1007%2f978-3-031-71530-3_2&partnerID=40&md5=aefee938cd5b8a74ee811a463d7409ae},
doi = {10.1007/978-3-031-71530-3_2},
isbn = {23673370 (ISSN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lecture Notes in Networks and Systems},
volume = {1140},
pages = {9–30},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The integration of Extended Reality (XR) technologies-Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)-promises to revolutionize education by offering immersive learning experiences. However, the complexity and resource intensity of content creation hinders the adoption of XR in educational contexts. This chapter explores Generative Artificial Intelligence (GenAI) as a solution, highlighting how GenAI models can facilitate the creation of educational XR content. GenAI enables educators to produce engaging XR experiences without needing advanced technical skills by automating aspects of the development process from ideation to deployment. Practical examples demonstrate GenAI’s current capability to generate assets and program applications, significantly lowering the barrier to creating personalized and interactive learning environments. The chapter also addresses challenges related to GenAI’s application in education, including technical limitations and ethical considerations. Ultimately, GenAI’s integration into XR content creation makes immersive educational experiences more accessible and practical, driven by only natural interactions, promising a future where technology-enhanced learning is universally attainable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Adversarial machine learning, Augmented Reality, Breakings, Content creation, Contrastive Learning, Development process, Educational context, Federated learning, Generative adversarial networks, Immersive learning, Intelligence models, Learning experiences, Mixed reality, Resource intensity, Technical skills, Virtual environments},
pubstate = {published},
tppubtype = {incollection}
}
The integration of Extended Reality (XR) technologies-Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)-promises to revolutionize education by offering immersive learning experiences. However, the complexity and resource intensity of content creation hinders the adoption of XR in educational contexts. This chapter explores Generative Artificial Intelligence (GenAI) as a solution, highlighting how GenAI models can facilitate the creation of educational XR content. GenAI enables educators to produce engaging XR experiences without needing advanced technical skills by automating aspects of the development process from ideation to deployment. Practical examples demonstrate GenAI’s current capability to generate assets and program applications, significantly lowering the barrier to creating personalized and interactive learning environments. The chapter also addresses challenges related to GenAI’s application in education, including technical limitations and ethical considerations. Ultimately, GenAI’s integration into XR content creation makes immersive educational experiences more accessible and practical, driven by only natural interactions, promising a future where technology-enhanced learning is universally attainable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Barbu, M.; Iordache, D. -D.; Petre, I.; Barbu, D. -C.; Băjenaru, L.
Framework Design for Reinforcing the Potential of XR Technologies in Transforming Inclusive Education Journal Article
In: Applied Sciences (Switzerland), vol. 15, no. 3, 2025, ISSN: 20763417 (ISSN).
Abstract | Links | BibTeX | Tags: Adaptive Learning, Adversarial machine learning, Artificial intelligence technologies, Augmented Reality, Contrastive Learning, Educational Technology, Extended reality (XR), Federated learning, Framework designs, Generative adversarial networks, Immersive, immersive experience, Immersive learning, Inclusive education, Learning platform, Special education needs
@article{barbu_framework_2025,
title = {Framework Design for Reinforcing the Potential of XR Technologies in Transforming Inclusive Education},
author = {M. Barbu and D. -D. Iordache and I. Petre and D. -C. Barbu and L. Băjenaru},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217742383&doi=10.3390%2fapp15031484&partnerID=40&md5=3148ff2a8a8fa1bef8094199cd6d32e3},
doi = {10.3390/app15031484},
issn = {20763417 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Applied Sciences (Switzerland)},
volume = {15},
number = {3},
abstract = {This study presents a novel approach to inclusive education by integrating augmented reality (XR) and generative artificial intelligence (AI) technologies into an immersive and adaptive learning platform designed for students with special educational needs. Building upon existing solutions, the approach uniquely combines XR and generative AI to facilitate personalized, accessible, and interactive learning experiences tailored to individual requirements. The framework incorporates an intuitive Unity XR-based interface alongside a generative AI module to enable near real-time customization of content and interactions. Additionally, the study examines related generative AI initiatives that promote inclusion through enhanced communication tools, educational support, and customizable assistive technologies. The motivation for this study arises from the pressing need to address the limitations of traditional educational methods, which often fail to meet the diverse needs of learners with special educational requirements. The integration of XR and generative AI offers transformative potential by creating adaptive, immersive, and inclusive learning environments. This approach ensures real-time adaptability to individual progress and accessibility, addressing critical barriers such as static content and lack of inclusivity in existing systems. The research outlines a pathway toward more inclusive and equitable education, significantly enhancing opportunities for learners with diverse needs and contributing to broader social integration and equity in education. © 2025 by the authors.},
keywords = {Adaptive Learning, Adversarial machine learning, Artificial intelligence technologies, Augmented Reality, Contrastive Learning, Educational Technology, Extended reality (XR), Federated learning, Framework designs, Generative adversarial networks, Immersive, immersive experience, Immersive learning, Inclusive education, Learning platform, Special education needs},
pubstate = {published},
tppubtype = {article}
}
This study presents a novel approach to inclusive education by integrating augmented reality (XR) and generative artificial intelligence (AI) technologies into an immersive and adaptive learning platform designed for students with special educational needs. Building upon existing solutions, the approach uniquely combines XR and generative AI to facilitate personalized, accessible, and interactive learning experiences tailored to individual requirements. The framework incorporates an intuitive Unity XR-based interface alongside a generative AI module to enable near real-time customization of content and interactions. Additionally, the study examines related generative AI initiatives that promote inclusion through enhanced communication tools, educational support, and customizable assistive technologies. The motivation for this study arises from the pressing need to address the limitations of traditional educational methods, which often fail to meet the diverse needs of learners with special educational requirements. The integration of XR and generative AI offers transformative potential by creating adaptive, immersive, and inclusive learning environments. This approach ensures real-time adaptability to individual progress and accessibility, addressing critical barriers such as static content and lack of inclusivity in existing systems. The research outlines a pathway toward more inclusive and equitable education, significantly enhancing opportunities for learners with diverse needs and contributing to broader social integration and equity in education. © 2025 by the authors.
Gao, H.; Xie, Y.; Kasneci, E.
PerVRML: ChatGPT-Driven Personalized VR Environments for Machine Learning Education Journal Article
In: International Journal of Human-Computer Interaction, 2025, ISSN: 10447318 (ISSN).
Abstract | Links | BibTeX | Tags: Backpropagation, ChatGPT, Curricula, Educational robots, Immersive learning, Interactive learning, Language Model, Large language model, large language models, Learning mode, Machine learning education, Machine-learning, Personalized learning, Support vector machines, Teaching, Virtual Reality, Virtual-reality environment, Virtualization
@article{gao_pervrml_2025,
title = {PerVRML: ChatGPT-Driven Personalized VR Environments for Machine Learning Education},
author = {H. Gao and Y. Xie and E. Kasneci},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005776517&doi=10.1080%2f10447318.2025.2504188&partnerID=40&md5=c2c59be3d20d02c6df7750c2330c8f6d},
doi = {10.1080/10447318.2025.2504188},
issn = {10447318 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Human-Computer Interaction},
abstract = {The advent of large language models (LLMs) such as ChatGPT has demonstrated significant potential for advancing educational technologies. Recently, growing interest has emerged in integrating ChatGPT with virtual reality (VR) to provide interactive and dynamic learning environments. This study explores the effectiveness of ChatGTP-driven VR in facilitating machine learning education through PerVRML. PerVRML incorporates a ChatGPT-powered avatar that provides real-time assistance and uses LLMs to personalize learning paths based on various sensor data from VR. A between-subjects design was employed to compare two learning modes: personalized and non-personalized. Quantitative data were collected from assessments, user experience surveys, and interaction metrics. The results indicate that while both learning modes supported learning effectively, ChatGPT-powered personalization significantly improved learning outcomes and had distinct impacts on user feedback. These findings underscore the potential of ChatGPT-enhanced VR to deliver adaptive and personalized educational experiences. © 2025 Taylor & Francis Group, LLC.},
keywords = {Backpropagation, ChatGPT, Curricula, Educational robots, Immersive learning, Interactive learning, Language Model, Large language model, large language models, Learning mode, Machine learning education, Machine-learning, Personalized learning, Support vector machines, Teaching, Virtual Reality, Virtual-reality environment, Virtualization},
pubstate = {published},
tppubtype = {article}
}
The advent of large language models (LLMs) such as ChatGPT has demonstrated significant potential for advancing educational technologies. Recently, growing interest has emerged in integrating ChatGPT with virtual reality (VR) to provide interactive and dynamic learning environments. This study explores the effectiveness of ChatGTP-driven VR in facilitating machine learning education through PerVRML. PerVRML incorporates a ChatGPT-powered avatar that provides real-time assistance and uses LLMs to personalize learning paths based on various sensor data from VR. A between-subjects design was employed to compare two learning modes: personalized and non-personalized. Quantitative data were collected from assessments, user experience surveys, and interaction metrics. The results indicate that while both learning modes supported learning effectively, ChatGPT-powered personalization significantly improved learning outcomes and had distinct impacts on user feedback. These findings underscore the potential of ChatGPT-enhanced VR to deliver adaptive and personalized educational experiences. © 2025 Taylor & Francis Group, LLC.
2024
Pester, A.; Tammaa, A.; Gütl, C.; Steinmaurer, A.; El-Seoud, S. A.
Conversational Agents, Virtual Worlds, and Beyond: A Review of Large Language Models Enabling Immersive Learning Proceedings Article
In: IEEE Global Eng. Edu. Conf., EDUCON, IEEE Computer Society, 2024, ISBN: 21659559 (ISSN); 979-835039402-3 (ISBN).
Abstract | Links | BibTeX | Tags: Computational Linguistics, Computer aided instruction, Conversational Agents, Education, Immersive learning, Language Model, Large language model, Learning systems, Literature reviews, LLM, Metaverse, Metaverses, Natural language processing systems, Pedagogy, Survey literature review, Virtual Reality, Virtual worlds
@inproceedings{pester_conversational_2024,
title = {Conversational Agents, Virtual Worlds, and Beyond: A Review of Large Language Models Enabling Immersive Learning},
author = {A. Pester and A. Tammaa and C. Gütl and A. Steinmaurer and S. A. El-Seoud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199068668&doi=10.1109%2fEDUCON60312.2024.10578895&partnerID=40&md5=1b904fd8a5e06d7ced42a328c028bbb7},
doi = {10.1109/EDUCON60312.2024.10578895},
isbn = {21659559 (ISSN); 979-835039402-3 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Global Eng. Edu. Conf., EDUCON},
publisher = {IEEE Computer Society},
abstract = {Large Language Models represent a significant breakthrough in Natural Language Processing research and opened a wide range of application domains. This paper demonstrates the successful integration of Large Language Models into immersive learning environments. The review highlights how this emerging technology aligns with pedagogical principles, enhancing the effectiveness of current educational systems. It also reflects recent advancements in integrating Large Language Models, including fine-tuning, hallucination reduction, fact-checking, and human evaluation of generated results. © 2024 IEEE.},
keywords = {Computational Linguistics, Computer aided instruction, Conversational Agents, Education, Immersive learning, Language Model, Large language model, Learning systems, Literature reviews, LLM, Metaverse, Metaverses, Natural language processing systems, Pedagogy, Survey literature review, Virtual Reality, Virtual worlds},
pubstate = {published},
tppubtype = {inproceedings}
}
Large Language Models represent a significant breakthrough in Natural Language Processing research and opened a wide range of application domains. This paper demonstrates the successful integration of Large Language Models into immersive learning environments. The review highlights how this emerging technology aligns with pedagogical principles, enhancing the effectiveness of current educational systems. It also reflects recent advancements in integrating Large Language Models, including fine-tuning, hallucination reduction, fact-checking, and human evaluation of generated results. © 2024 IEEE.
Gao, H.; Huai, H.; Yildiz-Degirmenci, S.; Bannert, M.; Kasneci, E.
DataliVR: Transformation of Data Literacy Education through Virtual Reality with ChatGPT-Powered Enhancements Proceedings Article
In: U., Eck; M., Sra; J., Stefanucci; M., Sugimoto; M., Tatzgern; I., Williams (Ed.): Proc. - IEEE Int. Symp. Mixed Augment. Real., ISMAR, pp. 120–129, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833151647-5 (ISBN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Chatbots, ChatGPT, Contrastive Learning, Data driven, Data literacy, Digital transformation, Federated learning, Immersive learning, Language Model, Large language model, Learning experiences, Learning outcome, LLMs, Virtual environments, Virtual Reality
@inproceedings{gao_datalivr_2024,
title = {DataliVR: Transformation of Data Literacy Education through Virtual Reality with ChatGPT-Powered Enhancements},
author = {H. Gao and H. Huai and S. Yildiz-Degirmenci and M. Bannert and E. Kasneci},
editor = {Eck U. and Sra M. and Stefanucci J. and Sugimoto M. and Tatzgern M. and Williams I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213525613&doi=10.1109%2fISMAR62088.2024.00026&partnerID=40&md5=abdeba7ecfecc8b1d715d633a29bd11d},
doi = {10.1109/ISMAR62088.2024.00026},
isbn = {979-833151647-5 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real., ISMAR},
pages = {120–129},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Data literacy is essential in today's data-driven world, emphasizing individuals' abilities to effectively manage data and extract meaningful insights. However, traditional classroom-based educational approaches often struggle to fully address the multifaceted nature of data literacy. As education undergoes digital transformation, innovative technologies such as Virtual Reality (VR) offer promising avenues for immersive and engaging learning experiences. This paper introduces DataliVR, a pioneering VR application aimed at enhancing the data literacy skills of university students within a contextual and gamified virtual learning environment. By integrating Large Language Models (LLMs) like ChatGPT as a conversational artificial intelligence (AI) chatbot embodied within a virtual avatar, DataliVR provides personalized learning assistance, enriching user learning experiences. Our study employed an experimental approach, with chatbot availability as the independent variable, analyzing learning experiences and outcomes as dependent variables with a sample of thirty participants. Our approach underscores the effectiveness and user-friendliness of ChatGPT-powered DataliVR in fostering data literacy skills. Moreover, our study examines the impact of the ChatGPT-based AI chatbot on users' learning, revealing significant effects on both learning experiences and outcomes. Our study presents a robust tool for fostering data literacy skills, contributing significantly to the digital advancement of data literacy education through cutting-edge VR and AI technologies. Moreover, our research provides valuable insights and implications for future research endeavors aiming to integrate LLMs (e.g., ChatGPT) into educational VR platforms. © 2024 IEEE.},
keywords = {Adversarial machine learning, Chatbots, ChatGPT, Contrastive Learning, Data driven, Data literacy, Digital transformation, Federated learning, Immersive learning, Language Model, Large language model, Learning experiences, Learning outcome, LLMs, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Data literacy is essential in today's data-driven world, emphasizing individuals' abilities to effectively manage data and extract meaningful insights. However, traditional classroom-based educational approaches often struggle to fully address the multifaceted nature of data literacy. As education undergoes digital transformation, innovative technologies such as Virtual Reality (VR) offer promising avenues for immersive and engaging learning experiences. This paper introduces DataliVR, a pioneering VR application aimed at enhancing the data literacy skills of university students within a contextual and gamified virtual learning environment. By integrating Large Language Models (LLMs) like ChatGPT as a conversational artificial intelligence (AI) chatbot embodied within a virtual avatar, DataliVR provides personalized learning assistance, enriching user learning experiences. Our study employed an experimental approach, with chatbot availability as the independent variable, analyzing learning experiences and outcomes as dependent variables with a sample of thirty participants. Our approach underscores the effectiveness and user-friendliness of ChatGPT-powered DataliVR in fostering data literacy skills. Moreover, our study examines the impact of the ChatGPT-based AI chatbot on users' learning, revealing significant effects on both learning experiences and outcomes. Our study presents a robust tool for fostering data literacy skills, contributing significantly to the digital advancement of data literacy education through cutting-edge VR and AI technologies. Moreover, our research provides valuable insights and implications for future research endeavors aiming to integrate LLMs (e.g., ChatGPT) into educational VR platforms. © 2024 IEEE.
Rahmani, R.; Westin, T.; Nevelsteen, K.
Future Healthcare in Generative AI with Real Metaverse Proceedings Article
In: E.E., Shakshuki (Ed.): Procedia Comput. Sci., pp. 487–493, Elsevier B.V., 2024, ISBN: 18770509 (ISSN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, AI, Augmented Reality, Autism spectrum disorders, Contrastive Learning, Diseases, Edge Intelligence, Generative adversarial networks, Healthcare, Immersive learning, Independent living systems, Language Model, Large language model, LLM, Metaverses, Posttraumatic stress disorder, Real Metaverse, Social challenges, Virtual environments
@inproceedings{rahmani_future_2024,
title = {Future Healthcare in Generative AI with Real Metaverse},
author = {R. Rahmani and T. Westin and K. Nevelsteen},
editor = {Shakshuki E.E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214986921&doi=10.1016%2fj.procs.2024.11.137&partnerID=40&md5=3e25f2a1b023cd49f59a066a96bb2dd0},
doi = {10.1016/j.procs.2024.11.137},
isbn = {18770509 (ISSN)},
year = {2024},
date = {2024-01-01},
booktitle = {Procedia Comput. Sci.},
volume = {251},
pages = {487–493},
publisher = {Elsevier B.V.},
abstract = {The Metaverse offers a simulated environment that could transform healthcare by providing immersive learning experiences through Internet application and social form that integrates network of virtual reality environments. The Metaverse is expected to contribute to a new way of socializing, where users can enter a verse as avatars. The concept allows avatars to switch between verses seamlessly. Virtual Reality (VR) in healthcare has shown promise for social-skill training, especially for individuals with Autism Spectrum Disorder (ASD) and social challenge training of patients with Post-Traumatic Stress Disorder (PTSD) requiring adaptable support. The problem lies in the limited adaptability and functionality of existing Metaverse implementations for individuals with ASD and PTSD. While studies have explored various implementation ideas, such as VR platforms for training social skills, social challenge and context-aware Augmented Reality (AR) systems for daily activities, many lack adaptability of user input and output. A proposed solution involves a context-aware system using AI, Large Language Models (LLMs) and generative agents to support independent living for individuals with ASD and a tool to enhance emotional learning with PTSD. © 2024 The Authors.},
keywords = {Adversarial machine learning, AI, Augmented Reality, Autism spectrum disorders, Contrastive Learning, Diseases, Edge Intelligence, Generative adversarial networks, Healthcare, Immersive learning, Independent living systems, Language Model, Large language model, LLM, Metaverses, Posttraumatic stress disorder, Real Metaverse, Social challenges, Virtual environments},
pubstate = {published},
tppubtype = {inproceedings}
}
The Metaverse offers a simulated environment that could transform healthcare by providing immersive learning experiences through Internet application and social form that integrates network of virtual reality environments. The Metaverse is expected to contribute to a new way of socializing, where users can enter a verse as avatars. The concept allows avatars to switch between verses seamlessly. Virtual Reality (VR) in healthcare has shown promise for social-skill training, especially for individuals with Autism Spectrum Disorder (ASD) and social challenge training of patients with Post-Traumatic Stress Disorder (PTSD) requiring adaptable support. The problem lies in the limited adaptability and functionality of existing Metaverse implementations for individuals with ASD and PTSD. While studies have explored various implementation ideas, such as VR platforms for training social skills, social challenge and context-aware Augmented Reality (AR) systems for daily activities, many lack adaptability of user input and output. A proposed solution involves a context-aware system using AI, Large Language Models (LLMs) and generative agents to support independent living for individuals with ASD and a tool to enhance emotional learning with PTSD. © 2024 The Authors.