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 expand the Abstract, Links and BibTex record for each paper.
2025
Aloudat, M. Z.; Aboumadi, A.; Soliman, A.; Al-Mohammed, H. A.; al-Ali, M.; Mahgoub, A.; Barhamgi, M.; Yaacoub, E.
Metaverse Unbound: A Survey on Synergistic Integration Between Semantic Communication, 6G, and Edge Learning Journal Article
In: IEEE Access, vol. 13, pp. 58302–58350, 2025, ISSN: 21693536 (ISSN), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: 6g wireless system, 6G wireless systems, Augmented Reality, Block-chain, Blockchain, Blockchain technology, Digital Twin Technology, Edge learning, Extended reality (XR), Language Model, Large language model, large language models (LLMs), Metaverse, Metaverses, Semantic communication, Virtual environments, Wireless systems
@article{aloudat_metaverse_2025,
title = {Metaverse Unbound: A Survey on Synergistic Integration Between Semantic Communication, 6G, and Edge Learning},
author = {M. Z. Aloudat and A. Aboumadi and A. Soliman and H. A. Al-Mohammed and M. al-Ali and A. Mahgoub and M. Barhamgi and E. Yaacoub},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003088610&doi=10.1109%2FACCESS.2025.3555753&partnerID=40&md5=c84a85efab6a29ee6916f5698922f720},
doi = {10.1109/ACCESS.2025.3555753},
issn = {21693536 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Access},
volume = {13},
pages = {58302–58350},
abstract = {With a focus on edge learning, blockchain, sixth generation (6G) wireless systems, semantic communication, and large language models (LLMs), this survey paper examines the revolutionary integration of cutting-edge technologies within the metaverse. This thorough examination highlights the critical role these technologies play in improving realism and user engagement on three main levels: technical, virtual, and physical. While the virtual layer focuses on building immersive experiences, the physical layer highlights improvements to the user interface through augmented reality (AR) goggles and virtual reality (VR) headsets. Blockchain-powered technical layer enables safe, decentralized communication. The survey highlights how the metaverse has the potential to drastically change how people interact in society by exploring applications in a variety of fields, such as immersive education, remote work, and entertainment. Concerns about privacy, scalability, and interoperability are raised, highlighting the necessity of continued study to realize the full potential of the metaverse. For scholars looking to broaden the reach and significance of the metaverse in the digital age, this paper is a useful tool. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {6g wireless system, 6G wireless systems, Augmented Reality, Block-chain, Blockchain, Blockchain technology, Digital Twin Technology, Edge learning, Extended reality (XR), Language Model, Large language model, large language models (LLMs), Metaverse, Metaverses, Semantic communication, Virtual environments, Wireless systems},
pubstate = {published},
tppubtype = {article}
}
With a focus on edge learning, blockchain, sixth generation (6G) wireless systems, semantic communication, and large language models (LLMs), this survey paper examines the revolutionary integration of cutting-edge technologies within the metaverse. This thorough examination highlights the critical role these technologies play in improving realism and user engagement on three main levels: technical, virtual, and physical. While the virtual layer focuses on building immersive experiences, the physical layer highlights improvements to the user interface through augmented reality (AR) goggles and virtual reality (VR) headsets. Blockchain-powered technical layer enables safe, decentralized communication. The survey highlights how the metaverse has the potential to drastically change how people interact in society by exploring applications in a variety of fields, such as immersive education, remote work, and entertainment. Concerns about privacy, scalability, and interoperability are raised, highlighting the necessity of continued study to realize the full potential of the metaverse. For scholars looking to broaden the reach and significance of the metaverse in the digital age, this paper is a useful tool. © 2025 Elsevier B.V., All rights reserved.
Nygren, T.; Samuelsson, M.; Hansson, P. -O.; Efimova, E.; Bachelder, S.
In: International Journal of Artificial Intelligence in Education, 2025, ISSN: 15604306 (ISSN); 15604292 (ISSN), (Publisher: Springer).
Abstract | Links | BibTeX | Tags: AI-generated feedback, Controversial issue in social study education, Controversial issues in social studies education, Curricula, Domain knowledge, Economic and social effects, Expert systems, Generative AI, Human engineering, Knowledge engineering, Language Model, Large language model, large language models (LLMs), Mixed reality, Mixed reality simulation, Mixed reality simulation (MRS), Pedagogical content knowledge, Pedagogical content knowledge (PCK), Personnel training, Preservice teachers, Social studies education, Teacher training, Teacher training simulation, Teacher training simulations, Teaching, Training simulation
@article{nygren_ai_2025,
title = {AI Versus Human Feedback in Mixed Reality Simulations: Comparing LLM and Expert Mentoring in Preservice Teacher Education on Controversial Issues},
author = {T. Nygren and M. Samuelsson and P. -O. Hansson and E. Efimova and S. Bachelder},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007244772&doi=10.1007%2Fs40593-025-00484-8&partnerID=40&md5=3404a614af6fe4d4d2cb284060600e3c},
doi = {10.1007/s40593-025-00484-8},
issn = {15604306 (ISSN); 15604292 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Artificial Intelligence in Education},
abstract = {This study explores the potential role of AI-generated mentoring within simulated environments designed for teacher education, specifically focused on the challenges of teaching controversial issues. Using a mixed-methods approach, we empirically investigate the potential and challenges of AI-generated feedback compared to that provided by human experts when mentoring preservice teachers in the context of mixed reality simulations. Findings reveal that human experts offered more mixed and nuanced feedback than ChatGPT-4o and Perplexity, especially when identifying missed teaching opportunities and balancing classroom discussions. The AI models evaluated were publicly available pro versions of LLMs and were tested using detailed prompts and coding schemes aligned with educational theories. AI systems were not very good at identifying aspects of general, pedagogical or content knowledge based on Shulman’s theories but were still quite effective in generating feedback in line with human experts. The study highlights the promise of AI to enhance teacher training but underscores the importance of combining AI feedback with expert insights to address the complexities of real-world teaching. This research contributes to a growing understanding of AI's potential role and limitations in education. It suggests that, while AI can be valuable to scale mixed reality simulations, it should be carefully evaluated and balanced by human expertise in teacher education. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Springer},
keywords = {AI-generated feedback, Controversial issue in social study education, Controversial issues in social studies education, Curricula, Domain knowledge, Economic and social effects, Expert systems, Generative AI, Human engineering, Knowledge engineering, Language Model, Large language model, large language models (LLMs), Mixed reality, Mixed reality simulation, Mixed reality simulation (MRS), Pedagogical content knowledge, Pedagogical content knowledge (PCK), Personnel training, Preservice teachers, Social studies education, Teacher training, Teacher training simulation, Teacher training simulations, Teaching, Training simulation},
pubstate = {published},
tppubtype = {article}
}
This study explores the potential role of AI-generated mentoring within simulated environments designed for teacher education, specifically focused on the challenges of teaching controversial issues. Using a mixed-methods approach, we empirically investigate the potential and challenges of AI-generated feedback compared to that provided by human experts when mentoring preservice teachers in the context of mixed reality simulations. Findings reveal that human experts offered more mixed and nuanced feedback than ChatGPT-4o and Perplexity, especially when identifying missed teaching opportunities and balancing classroom discussions. The AI models evaluated were publicly available pro versions of LLMs and were tested using detailed prompts and coding schemes aligned with educational theories. AI systems were not very good at identifying aspects of general, pedagogical or content knowledge based on Shulman’s theories but were still quite effective in generating feedback in line with human experts. The study highlights the promise of AI to enhance teacher training but underscores the importance of combining AI feedback with expert insights to address the complexities of real-world teaching. This research contributes to a growing understanding of AI's potential role and limitations in education. It suggests that, while AI can be valuable to scale mixed reality simulations, it should be carefully evaluated and balanced by human expertise in teacher education. © 2025 Elsevier B.V., All rights reserved.
Ahmed, Y.; Eissa, A.; Harb, O.; Miniesy, O.; Miniesy, Z.; Noureldin, M.; Mougy, A. E.
From Abstract Prompts to Cybersecurity Labs: Automating Virtual Environment Design and Deployment with Multi-Agent Systems and LLM-Driven Orchestration Proceedings Article
In: Alsmirat, M.; Alkhabbas, F.; Al-Abdullah, M.; Jararweh, Y. (Ed.): pp. 99–107, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798350392920 (ISBN).
Abstract | Links | BibTeX | Tags: Automated scenario generation, Containerization, Containers, Cybe range, cyber ranges, Cyber security, Cybersecurity, Cybersecurity training, Docker, Environment generation cybe range lab generation, environment generation cyber range lab generation, Intelligent Agents, Language Model, Large language model, large language models (LLMs), Multi agent systems, Multi-agent systems, Multiagent systems (MASs), Network Security, Personnel training, Quality assurance, Scalability, Scenarios generation, Virtual Reality
@inproceedings{ahmed_abstract_2025,
title = {From Abstract Prompts to Cybersecurity Labs: Automating Virtual Environment Design and Deployment with Multi-Agent Systems and LLM-Driven Orchestration},
author = {Y. Ahmed and A. Eissa and O. Harb and O. Miniesy and Z. Miniesy and M. Noureldin and A. E. Mougy},
editor = {M. Alsmirat and F. Alkhabbas and M. Al-Abdullah and Y. Jararweh},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105016685428&doi=10.1109%2FICSC65596.2025.11140357&partnerID=40&md5=053515e92d70c5c694cc8ba888f39afa},
doi = {10.1109/ICSC65596.2025.11140357},
isbn = {9798350392920 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {99–107},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Cyber ranges are essential for cybersecurity training, but current systems face challenges like resource-intensive infrastructures, static configurations, and laborious setups, limiting scalability and accessibility, especially for educational institutions with fewer resources. To address these issues, this paper introduces a containerized multi-agent system that automates the design and deployment of cybersecurity training environments. Using large language models (LLMs) for scenario orchestration, the system transforms natural language prompts into fully functional environments through Docker containerization. It features three specialized agents: a master agent for scenario planning, machine worker agents for environment and vulnerability generation, and a quality assurance agent for validation and debugging, ensuring modularity, scalability, and precision. Evaluated across 14 diverse attack scenarios, the system demonstrates high accuracy in generating web vulnerabilities, network exploits, and multi-step attacks. By automating scenario creation and deployment, this system enhances cybersecurity education and training, bridging critical gaps and offering a scalable, adaptive, and resource-efficient solution to meet the growing demand for skilled cybersecurity professionals. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Automated scenario generation, Containerization, Containers, Cybe range, cyber ranges, Cyber security, Cybersecurity, Cybersecurity training, Docker, Environment generation cybe range lab generation, environment generation cyber range lab generation, Intelligent Agents, Language Model, Large language model, large language models (LLMs), Multi agent systems, Multi-agent systems, Multiagent systems (MASs), Network Security, Personnel training, Quality assurance, Scalability, Scenarios generation, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Cyber ranges are essential for cybersecurity training, but current systems face challenges like resource-intensive infrastructures, static configurations, and laborious setups, limiting scalability and accessibility, especially for educational institutions with fewer resources. To address these issues, this paper introduces a containerized multi-agent system that automates the design and deployment of cybersecurity training environments. Using large language models (LLMs) for scenario orchestration, the system transforms natural language prompts into fully functional environments through Docker containerization. It features three specialized agents: a master agent for scenario planning, machine worker agents for environment and vulnerability generation, and a quality assurance agent for validation and debugging, ensuring modularity, scalability, and precision. Evaluated across 14 diverse attack scenarios, the system demonstrates high accuracy in generating web vulnerabilities, network exploits, and multi-step attacks. By automating scenario creation and deployment, this system enhances cybersecurity education and training, bridging critical gaps and offering a scalable, adaptive, and resource-efficient solution to meet the growing demand for skilled cybersecurity professionals. © 2025 Elsevier B.V., All rights reserved.
Wang, R. -G.; Tsai, C. H.; Tseng, M. C.; Hong, R. -C.; Syu, H.; Chou, C. -C.
Immersive Smart Meter Data Analytics: Leveraging eXtended Reality with LSTM and LLMs Proceedings Article
In: pp. 32–36, International Workshop on Computer Science and Engineering (WCSE), 2025.
Abstract | Links | BibTeX | Tags: Data Analytics, Data visualization, Decision making, Energy management, Energy-consumption, Exponential growth, Extended reality (XR), Forecasting, Human computer interaction, Immersive, Language Model, Large language model, large language models (LLMs), Long short-term memory, Long Short-Term Memory (LSTM), Short term memory, Smart Grid technologies, Smart Meters, Smart power grids, Visual analytics
@inproceedings{wang_immersive_2025,
title = {Immersive Smart Meter Data Analytics: Leveraging eXtended Reality with LSTM and LLMs},
author = {R. -G. Wang and C. H. Tsai and M. C. Tseng and R. -C. Hong and H. Syu and C. -C. Chou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017965008&doi=10.18178%2Fwcse.2025.06.006&partnerID=40&md5=866ab1ca8cdf0372c020f0131f1d68c1},
doi = {10.18178/wcse.2025.06.006},
year = {2025},
date = {2025-01-01},
pages = {32–36},
publisher = {International Workshop on Computer Science and Engineering (WCSE)},
abstract = {The rapid advancement of smart grid technologies has led to an exponential growth in smart meter data, creating new opportunities for more accurate energy consumption forecasting and immersive data visualization. This study proposes an integrated framework that combines eXtended Reality (XR), Long Short-Term Memory (LSTM) networks, and Large Language Models (LLMs) to enhance smart meter data analytics. The process begins with the application of LSTM to capture temporal dependencies in historical electricity usage data. Subsequently, the Large Language Models (LLMs) are employed to refine these textual forecasts, offering better predictions and explanations that are easily understandable by end-users. Finally, the enriched insights are presented through an XR environment, enabling users to interact with smart meter analytics in an immersive and intuitive way. By visualizing data trends, predictions, and explanatory narratives in a spatial computing interface, users can explore complex information more effectively. This multi-modal approach facilitates better decision-making for energy management, promotes user engagement, and supports smart city initiatives aiming for sustainable energy consumption. The integration of XR, LSTM, and LLMs technologies demonstrates a promising direction for future research and practical applications in smart energy systems. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Data Analytics, Data visualization, Decision making, Energy management, Energy-consumption, Exponential growth, Extended reality (XR), Forecasting, Human computer interaction, Immersive, Language Model, Large language model, large language models (LLMs), Long short-term memory, Long Short-Term Memory (LSTM), Short term memory, Smart Grid technologies, Smart Meters, Smart power grids, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapid advancement of smart grid technologies has led to an exponential growth in smart meter data, creating new opportunities for more accurate energy consumption forecasting and immersive data visualization. This study proposes an integrated framework that combines eXtended Reality (XR), Long Short-Term Memory (LSTM) networks, and Large Language Models (LLMs) to enhance smart meter data analytics. The process begins with the application of LSTM to capture temporal dependencies in historical electricity usage data. Subsequently, the Large Language Models (LLMs) are employed to refine these textual forecasts, offering better predictions and explanations that are easily understandable by end-users. Finally, the enriched insights are presented through an XR environment, enabling users to interact with smart meter analytics in an immersive and intuitive way. By visualizing data trends, predictions, and explanatory narratives in a spatial computing interface, users can explore complex information more effectively. This multi-modal approach facilitates better decision-making for energy management, promotes user engagement, and supports smart city initiatives aiming for sustainable energy consumption. The integration of XR, LSTM, and LLMs technologies demonstrates a promising direction for future research and practical applications in smart energy systems. © 2025 Elsevier B.V., All rights reserved.
Bendarkawi, J.; Ponce, A.; Mata, S. C.; Aliu, A.; Liu, Y.; Zhang, L.; Liaqat, A.; Rao, V. N. Nagaraj; Monroy-Hernández, A.
ConversAR: Exploring Embodied LLM-Powered Group Conversations in Augmented Reality for Second Language Learners 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 (AR), Embodied agent, Embodied Agents, Language learning, Language Model, Large language model, large language models (LLMs), Population dynamics, Second language, Second Language Acquisition, Second language learners, Social dynamics, Turn-taking
@inproceedings{bendarkawi_conversar_2025,
title = {ConversAR: Exploring Embodied LLM-Powered Group Conversations in Augmented Reality for Second Language Learners},
author = {J. Bendarkawi and A. Ponce and S. C. Mata and A. Aliu and Y. Liu and L. Zhang and A. Liaqat and V. N. Nagaraj Rao and A. Monroy-Hernández},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005746128&doi=10.1145%2F3706599.3720162&partnerID=40&md5=af41051bf92ba810e92ccc7d70e4ff45},
doi = {10.1145/3706599.3720162},
isbn = {9798400713958 (ISBN); 9798400713941 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf Hum Fact Comput Syst Proc},
publisher = {Association for Computing Machinery},
abstract = {Group conversations are valuable for second language (L2) learners as they provide opportunities to practice listening and speaking, exercise complex turn-taking skills, and experience group social dynamics in a target language. However, most existing Augmented Reality (AR)-based conversational learning tools focus on dyadic interactions rather than group dialogues. Although research has shown that AR can help reduce speaking anxiety and create a comfortable space for practicing speaking skills in dyadic scenarios, especially with Large Language Model (LLM)-based conversational agents, the potential for group language practice using these technologies remains largely unexplored. We introduce ConversAR, a gpt-4o powered AR application, that enables L2 learners to practice contextualized group conversations. Our system features two embodied LLM agents with vision-based scene understanding and live captions. In a system evaluation with 10 participants, users reported reduced speaking anxiety and increased learner autonomy compared to perceptions of in-person practice methods with other learners. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Augmented Reality, Augmented Reality (AR), Embodied agent, Embodied Agents, Language learning, Language Model, Large language model, large language models (LLMs), Population dynamics, Second language, Second Language Acquisition, Second language learners, Social dynamics, Turn-taking},
pubstate = {published},
tppubtype = {inproceedings}
}
Group conversations are valuable for second language (L2) learners as they provide opportunities to practice listening and speaking, exercise complex turn-taking skills, and experience group social dynamics in a target language. However, most existing Augmented Reality (AR)-based conversational learning tools focus on dyadic interactions rather than group dialogues. Although research has shown that AR can help reduce speaking anxiety and create a comfortable space for practicing speaking skills in dyadic scenarios, especially with Large Language Model (LLM)-based conversational agents, the potential for group language practice using these technologies remains largely unexplored. We introduce ConversAR, a gpt-4o powered AR application, that enables L2 learners to practice contextualized group conversations. Our system features two embodied LLM agents with vision-based scene understanding and live captions. In a system evaluation with 10 participants, users reported reduced speaking anxiety and increased learner autonomy compared to perceptions of in-person practice methods with other learners. © 2025 Elsevier B.V., All rights reserved.