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
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).
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=8f3f9421ce2d6be57f8154a122ee192c},
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. © 2013 IEEE.},
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. © 2013 IEEE.
Nygren, T.; Samuelsson, M.; Hansson, P. -O.; Efimova, E.; Bachelder, S.
In: International Journal of Artificial Intelligence in Education, 2025, ISSN: 15604292 (ISSN).
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=d3cb14a8117045505cbbeb174b32b88d},
doi = {10.1007/s40593-025-00484-8},
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. © The Author(s) 2025.},
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. © The Author(s) 2025.
Bendarkawi, J.; Ponce, A.; Mata, S. C.; Aliu, A.; Liu, Y.; Zhang, L.; Liaqat, A.; Rao, V. N.; 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: 979-840071395-8 (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. 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=8330d3e0cb735caffa828b848ab9a110},
doi = {10.1145/3706599.3720162},
isbn = {979-840071395-8 (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 Copyright held by the owner/author(s).},
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 Copyright held by the owner/author(s).