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
Nguyen, A.; Gul, F.; Dang, B.; Huynh, L.; Tuunanen, T.
Designing embodied generative artificial intelligence in mixed reality for active learning in higher education Journal Article
In: Innovations in Education and Teaching International, 2025, ISSN: 14703297 (ISSN).
Abstract | Links | BibTeX | Tags: Active learning, Generative AI, higher education, Mixed reality, Self-regulated learning
@article{nguyen_designing_2025,
title = {Designing embodied generative artificial intelligence in mixed reality for active learning in higher education},
author = {A. Nguyen and F. Gul and B. Dang and L. Huynh and T. Tuunanen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004906187&doi=10.1080%2f14703297.2025.2499177&partnerID=40&md5=4a59b74e6278024ec9dadf9ad9e1a50d},
doi = {10.1080/14703297.2025.2499177},
issn = {14703297 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Innovations in Education and Teaching International},
abstract = {Generative Artificial Intelligence (GenAI) technologies have introduced significant changes to higher education, but the role of Embodied GenAI Agents in Mixed Reality (MR) environments is still relatively unexplored. This study was carried out to develop an embodied GenAI system designed to facilitate active learning, self-regulated learning and enhance human-AI shared regulation in educational settings. The study also aimed to understand how adult learners engage with and perceive these anthropomorphic agents in an immersive MR setting, with a particular focus on their effects on active learning and cognitive load. Using an echeloned Design Science Research (eDSR) approach, we developed an MR learning experience incorporating an Embodied GenAI Agent. The application was demonstrated with 26 higher education learners through questionnaires and observational recordings. Our study contributes to the ongoing design and development of AI-based educational tools, with the potential to afford more active and agentic learning experiences. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.},
keywords = {Active learning, Generative AI, higher education, Mixed reality, Self-regulated learning},
pubstate = {published},
tppubtype = {article}
}
Generative Artificial Intelligence (GenAI) technologies have introduced significant changes to higher education, but the role of Embodied GenAI Agents in Mixed Reality (MR) environments is still relatively unexplored. This study was carried out to develop an embodied GenAI system designed to facilitate active learning, self-regulated learning and enhance human-AI shared regulation in educational settings. The study also aimed to understand how adult learners engage with and perceive these anthropomorphic agents in an immersive MR setting, with a particular focus on their effects on active learning and cognitive load. Using an echeloned Design Science Research (eDSR) approach, we developed an MR learning experience incorporating an Embodied GenAI Agent. The application was demonstrated with 26 higher education learners through questionnaires and observational recordings. Our study contributes to the ongoing design and development of AI-based educational tools, with the potential to afford more active and agentic learning experiences. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
2024
Jia, Y.; Sin, Z. P. T.; Wang, X. E.; Li, C.; Ng, P. H. F.; Huang, X.; Dong, J.; Wang, Y.; Baciu, G.; Cao, J.; Li, Q.
NivTA: Towards a Naturally Interactable Edu-Metaverse Teaching Assistant for CAVE Proceedings Article
In: Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom, pp. 57–64, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833151599-7 (ISBN).
Abstract | Links | BibTeX | Tags: Active learning, Adversarial machine learning, cave automatic virtual environment, Cave automatic virtual environments, Caves, Chatbots, Contrastive Learning, Digital elevation model, Federated learning, Interactive education, Language Model, Large language model agent, Learning Activity, LLM agents, Metaverses, Model agents, Natural user interface, Students, Teaching, Teaching assistants, Virtual environments, Virtual Reality, virtual teaching assistant, Virtual teaching assistants
@inproceedings{jia_nivta_2024,
title = {NivTA: Towards a Naturally Interactable Edu-Metaverse Teaching Assistant for CAVE},
author = {Y. Jia and Z. P. T. Sin and X. E. Wang and C. Li and P. H. F. Ng and X. Huang and J. Dong and Y. Wang and G. Baciu and J. Cao and Q. Li},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211447638&doi=10.1109%2fMetaCom62920.2024.00023&partnerID=40&md5=efefd453c426e74705518254bdc49e87},
doi = {10.1109/MetaCom62920.2024.00023},
isbn = {979-833151599-7 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom},
pages = {57–64},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Edu-metaverse is a specialized metaverse dedicated for interactive education in an immersive environment. Its main purpose is to immerse the learners in a digital environment and conduct learning activities that could mirror reality. Not only does it enable activities that may be difficult to perform in the real world, but it also extends the interaction to personalized and CL. This is a more effective pedagogical approach as it tends to enhance the motivation and engagement of students and it increases their active participation in lessons delivered. To this extend, we propose to realize an interactive virtual teaching assistant called NivTA. To make NivTA easily accessible and engaging by multiple users simultaneously, we also propose to use a CAVE virtual environment (CAVE-VR) as a "metaverse window"into concepts, ideas, topics, and learning activities. The students simply need to step into the CAVE-VR and interact with a life-size teaching assistant that they can engage with naturally, as if they are approaching a real person. Instead of text-based interaction currently developed for large language models (LLM), NivTA is given additional cues regarding the users so it can react more naturally via a specific prompt design. For example, the user can simply point to an educational concept and ask NivTA to explain what it is. To guide NivTA onto the educational concept, the prompt is also designed to feed in an educational KG to provide NivTA with the context of the student's question. The NivTA system is an integration of several components that are discussed in this paper. We further describe how the system is designed and implemented, along with potential applications and future work on interactive collaborative edu-metaverse environments dedicated for teaching and learning. © 2024 IEEE.},
keywords = {Active learning, Adversarial machine learning, cave automatic virtual environment, Cave automatic virtual environments, Caves, Chatbots, Contrastive Learning, Digital elevation model, Federated learning, Interactive education, Language Model, Large language model agent, Learning Activity, LLM agents, Metaverses, Model agents, Natural user interface, Students, Teaching, Teaching assistants, Virtual environments, Virtual Reality, virtual teaching assistant, Virtual teaching assistants},
pubstate = {published},
tppubtype = {inproceedings}
}
Edu-metaverse is a specialized metaverse dedicated for interactive education in an immersive environment. Its main purpose is to immerse the learners in a digital environment and conduct learning activities that could mirror reality. Not only does it enable activities that may be difficult to perform in the real world, but it also extends the interaction to personalized and CL. This is a more effective pedagogical approach as it tends to enhance the motivation and engagement of students and it increases their active participation in lessons delivered. To this extend, we propose to realize an interactive virtual teaching assistant called NivTA. To make NivTA easily accessible and engaging by multiple users simultaneously, we also propose to use a CAVE virtual environment (CAVE-VR) as a "metaverse window"into concepts, ideas, topics, and learning activities. The students simply need to step into the CAVE-VR and interact with a life-size teaching assistant that they can engage with naturally, as if they are approaching a real person. Instead of text-based interaction currently developed for large language models (LLM), NivTA is given additional cues regarding the users so it can react more naturally via a specific prompt design. For example, the user can simply point to an educational concept and ask NivTA to explain what it is. To guide NivTA onto the educational concept, the prompt is also designed to feed in an educational KG to provide NivTA with the context of the student's question. The NivTA system is an integration of several components that are discussed in this paper. We further describe how the system is designed and implemented, along with potential applications and future work on interactive collaborative edu-metaverse environments dedicated for teaching and learning. © 2024 IEEE.