AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our publications.
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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
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},
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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.
2024
Salloum, A.; Alfaisal, R.; Salloum, S. A.
Revolutionizing Medical Education: Empowering Learning with ChatGPT Book Section
In: Studies in Big Data, vol. 144, pp. 79–90, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 21976503 (ISSN).
Abstract | Links | BibTeX | Tags: Abstracting, AI integration, ChatGPT, Education, Human like, Interactivity, Language Model, Learning platform, Learning platforms, Medical education, Metaverse, Metaverses, Paradigm shifts, Personalizations, Technological advancement
@incollection{salloum_revolutionizing_2024,
title = {Revolutionizing Medical Education: Empowering Learning with ChatGPT},
author = {A. Salloum and R. Alfaisal and S. A. Salloum},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191302844&doi=10.1007%2f978-3-031-52280-2_6&partnerID=40&md5=a5325b8e43460906174a3c7a2c383e1a},
doi = {10.1007/978-3-031-52280-2_6},
isbn = {21976503 (ISSN)},
year = {2024},
date = {2024-01-01},
booktitle = {Studies in Big Data},
volume = {144},
pages = {79–90},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The landscape of medical education is undergoing a paradigm shift driven by technological advancements. This abstract explores the potential of ChatGPT, an advanced AI language model developed by OpenAI, in revolutionizing medical education. ChatGPT’s capacity to understand and generate human-like text opens doors to interactive, personalized, and adaptive learning experiences that address the evolving demands of medical training. Medical education traditionally relies on didactic approaches that often lack interactivity and personalization. ChatGPT addresses this limitation by introducing a conversational AI-driven dimension to medical learning. Learners can engage with ChatGPT in natural language, seeking explanations, asking questions, and clarifying doubts. This adaptive interactivity mirrors the dynamic nature of medical practice and fosters critical thinking skills essential for medical professionals. Furthermore, ChatGPT augments educators’ roles by assisting in content creation, formative assessments, and immediate feedback delivery. This empowers educators to focus on higher-order facilitation and mentorship, enriching the learning journey. However, responsible integration of ChatGPT into medical education demands careful curation of accurate medical content and validation against trusted sources. Ethical considerations related to AI-generated content and potential biases also warrant attention. This abstract underscores the transformative potential of ChatGPT in reshaping medical education. By creating an environment of engagement, adaptability, and personalization, ChatGPT paves the way for a dynamic and empowered medical learning ecosystem that aligns with the demands of modern healthcare. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Abstracting, AI integration, ChatGPT, Education, Human like, Interactivity, Language Model, Learning platform, Learning platforms, Medical education, Metaverse, Metaverses, Paradigm shifts, Personalizations, Technological advancement},
pubstate = {published},
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The landscape of medical education is undergoing a paradigm shift driven by technological advancements. This abstract explores the potential of ChatGPT, an advanced AI language model developed by OpenAI, in revolutionizing medical education. ChatGPT’s capacity to understand and generate human-like text opens doors to interactive, personalized, and adaptive learning experiences that address the evolving demands of medical training. Medical education traditionally relies on didactic approaches that often lack interactivity and personalization. ChatGPT addresses this limitation by introducing a conversational AI-driven dimension to medical learning. Learners can engage with ChatGPT in natural language, seeking explanations, asking questions, and clarifying doubts. This adaptive interactivity mirrors the dynamic nature of medical practice and fosters critical thinking skills essential for medical professionals. Furthermore, ChatGPT augments educators’ roles by assisting in content creation, formative assessments, and immediate feedback delivery. This empowers educators to focus on higher-order facilitation and mentorship, enriching the learning journey. However, responsible integration of ChatGPT into medical education demands careful curation of accurate medical content and validation against trusted sources. Ethical considerations related to AI-generated content and potential biases also warrant attention. This abstract underscores the transformative potential of ChatGPT in reshaping medical education. By creating an environment of engagement, adaptability, and personalization, ChatGPT paves the way for a dynamic and empowered medical learning ecosystem that aligns with the demands of modern healthcare. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.