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 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
Wei, X.; Chen, Y.; Zhao, P.; Wang, L.; Lee, L. -K.; Liu, R.
In: Interactive Learning Environments, 2025, ISSN: 10494820 (ISSN).
Abstract | Links | BibTeX | Tags: 5E learning model, generative artificial intelligence, Immersive virtual reality, Pedagogical agents, primary students, Science education
@article{wei_effects_2025,
title = {Effects of immersive virtual reality on primary students’ science performance in classroom settings: a generative AI pedagogical agents-enhanced 5E approach},
author = {X. Wei and Y. Chen and P. Zhao and L. Wang and L. -K. Lee and R. Liu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007642085&doi=10.1080%2f10494820.2025.2514101&partnerID=40&md5=94fee41fcdce74ebb9e91c6430ed9507},
doi = {10.1080/10494820.2025.2514101},
issn = {10494820 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Interactive Learning Environments},
abstract = {Immersive virtual reality (IVR) holds the potential to transform science education by offering opportunities to enhance learners’ engagement, motivation, and conceptual understanding. However, the integration of generative AI pedagogical agents (GPAs) into IVR environments remains underexplored. Specifically, the application of GPAs as a scaffold within the framework of the 5E learning model in science education has not been fully examined. To address these gaps, this study explored the impact of a GPA-enhanced 5E (GPA-5E) learning approach in IVR on primary students’ academic achievement, self-efficacy, collective efficacy, and their perceptions of the proposed method. Adopting a mixed-methods design, eighty sixth-grade students from two complete classes were assigned to either an experimental group engaging IVR science learning with a GPA-5E approach or a control group following the traditional 5E method. The results indicated that the GPA-5E approach in IVR science learning significantly improved students’ academic achievement, self-efficacy, and collective efficacy compared to the traditional method. Students in the experimental group also reported positive perceptions of the GPA-5E method, emphasizing its benefits in IVR science learning. These findings underscore the potential of integrating GPA-enhanced scaffolds within IVR environments to enrich pedagogical strategies and improve student outcomes in science education. © 2025 Informa UK Limited, trading as Taylor & Francis Group.},
keywords = {5E learning model, generative artificial intelligence, Immersive virtual reality, Pedagogical agents, primary students, Science education},
pubstate = {published},
tppubtype = {article}
}
Immersive virtual reality (IVR) holds the potential to transform science education by offering opportunities to enhance learners’ engagement, motivation, and conceptual understanding. However, the integration of generative AI pedagogical agents (GPAs) into IVR environments remains underexplored. Specifically, the application of GPAs as a scaffold within the framework of the 5E learning model in science education has not been fully examined. To address these gaps, this study explored the impact of a GPA-enhanced 5E (GPA-5E) learning approach in IVR on primary students’ academic achievement, self-efficacy, collective efficacy, and their perceptions of the proposed method. Adopting a mixed-methods design, eighty sixth-grade students from two complete classes were assigned to either an experimental group engaging IVR science learning with a GPA-5E approach or a control group following the traditional 5E method. The results indicated that the GPA-5E approach in IVR science learning significantly improved students’ academic achievement, self-efficacy, and collective efficacy compared to the traditional method. Students in the experimental group also reported positive perceptions of the GPA-5E method, emphasizing its benefits in IVR science learning. These findings underscore the potential of integrating GPA-enhanced scaffolds within IVR environments to enrich pedagogical strategies and improve student outcomes in science education. © 2025 Informa UK Limited, trading as Taylor & Francis Group.
Logothetis, I.; Diakogiannis, K.; Vidakis, N.
Interactive Learning Through Conversational Avatars and Immersive VR: Enhancing Diabetes Education and Self-Management Proceedings Article
In: X., Fang (Ed.): Lect. Notes Comput. Sci., pp. 415–429, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303192577-1 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Chronic disease, Computer aided instruction, Diabetes Education, Diagnosis, E-Learning, Education management, Engineering education, Gamification, Immersive virtual reality, Interactive computer graphics, Interactive learning, Large population, Learning systems, NUI, Self management, Serious game, Serious games, simulation, Virtual Reality
@inproceedings{logothetis_interactive_2025,
title = {Interactive Learning Through Conversational Avatars and Immersive VR: Enhancing Diabetes Education and Self-Management},
author = {I. Logothetis and K. Diakogiannis and N. Vidakis},
editor = {Fang X.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105008266480&doi=10.1007%2f978-3-031-92578-8_27&partnerID=40&md5=451274dfa3ef0b3f1b39c7d5a665ee3b},
doi = {10.1007/978-3-031-92578-8_27},
isbn = {03029743 (ISSN); 978-303192577-1 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15816 LNCS},
pages = {415–429},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Diabetes is a chronic disease affecting a large population of the world. Education and self-management of diabetes are crucial. Technologies such as Virtual Reality (VR) have presented promising results in healthcare education, while studies suggest that Artificial Intelligence (AI) can help in learning by further engaging the learner. This study aims to educate users on the entire routine of managing diabetes. The serious game utilizes VR for realistic interaction with diabetes tools and generative AI through a conversational avatar that acts as an assistant instructor. In this way, it allows users to practice diagnostic and therapeutic interventions in a controlled virtual environment, helping to build their understanding and confidence in diabetes management. To measure the effects of the proposed serious game, presence, and perceived agency were measured. Preliminary results indicate that this setup aids in the engagement and immersion of learners, while the avatar can provide helpful information during gameplay. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Artificial intelligence, Chronic disease, Computer aided instruction, Diabetes Education, Diagnosis, E-Learning, Education management, Engineering education, Gamification, Immersive virtual reality, Interactive computer graphics, Interactive learning, Large population, Learning systems, NUI, Self management, Serious game, Serious games, simulation, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Diabetes is a chronic disease affecting a large population of the world. Education and self-management of diabetes are crucial. Technologies such as Virtual Reality (VR) have presented promising results in healthcare education, while studies suggest that Artificial Intelligence (AI) can help in learning by further engaging the learner. This study aims to educate users on the entire routine of managing diabetes. The serious game utilizes VR for realistic interaction with diabetes tools and generative AI through a conversational avatar that acts as an assistant instructor. In this way, it allows users to practice diagnostic and therapeutic interventions in a controlled virtual environment, helping to build their understanding and confidence in diabetes management. To measure the effects of the proposed serious game, presence, and perceived agency were measured. Preliminary results indicate that this setup aids in the engagement and immersion of learners, while the avatar can provide helpful information during gameplay. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
2024
Chheang, V.; Sharmin, S.; Marquez-Hernandez, R.; Patel, M.; Rajasekaran, D.; Caulfield, G.; Kiafar, B.; Li, J.; Kullu, P.; Barmaki, R. L.
Towards Anatomy Education with Generative AI-based Virtual Assistants in Immersive Virtual Reality Environments Proceedings Article
In: Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR, pp. 21–30, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037202-1 (ISBN).
Abstract | Links | BibTeX | Tags: 3-D visualization systems, Anatomy education, Anatomy educations, Cognitive complexity, E-Learning, Embodied virtual assistant, Embodied virtual assistants, Generative AI, generative artificial intelligence, Human computer interaction, human-computer interaction, Immersive virtual reality, Interactive 3d visualizations, Knowledge Management, Medical education, Three dimensional computer graphics, Verbal communications, Virtual assistants, Virtual Reality, Virtual-reality environment
@inproceedings{chheang_towards_2024,
title = {Towards Anatomy Education with Generative AI-based Virtual Assistants in Immersive Virtual Reality Environments},
author = {V. Chheang and S. Sharmin and R. Marquez-Hernandez and M. Patel and D. Rajasekaran and G. Caulfield and B. Kiafar and J. Li and P. Kullu and R. L. Barmaki},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187216893&doi=10.1109%2fAIxVR59861.2024.00011&partnerID=40&md5=33e8744309add5fe400f4f341326505f},
doi = {10.1109/AIxVR59861.2024.00011},
isbn = {979-835037202-1 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},
pages = {21–30},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Virtual reality (VR) and interactive 3D visualization systems have enhanced educational experiences and environments, particularly in complicated subjects such as anatomy education. VR-based systems surpass the potential limitations of traditional training approaches in facilitating interactive engagement among students. However, research on embodied virtual assistants that leverage generative artificial intelligence (AI) and verbal communication in the anatomy education context is underrepresented. In this work, we introduce a VR environment with a generative AI-embodied virtual assistant to support participants in responding to varying cognitive complexity anatomy questions and enable verbal communication. We assessed the technical efficacy and usability of the proposed environment in a pilot user study with 16 participants. We conducted a within-subject design for virtual assistant configuration (avatar- and screen-based), with two levels of cognitive complexity (knowledge- and analysis-based). The results reveal a significant difference in the scores obtained from knowledge- and analysis-based questions in relation to avatar configuration. Moreover, results provide insights into usability, cognitive task load, and the sense of presence in the proposed virtual assistant configurations. Our environment and results of the pilot study offer potential benefits and future research directions beyond medical education, using generative AI and embodied virtual agents as customized virtual conversational assistants. © 2024 IEEE.},
keywords = {3-D visualization systems, Anatomy education, Anatomy educations, Cognitive complexity, E-Learning, Embodied virtual assistant, Embodied virtual assistants, Generative AI, generative artificial intelligence, Human computer interaction, human-computer interaction, Immersive virtual reality, Interactive 3d visualizations, Knowledge Management, Medical education, Three dimensional computer graphics, Verbal communications, Virtual assistants, Virtual Reality, Virtual-reality environment},
pubstate = {published},
tppubtype = {inproceedings}
}
Virtual reality (VR) and interactive 3D visualization systems have enhanced educational experiences and environments, particularly in complicated subjects such as anatomy education. VR-based systems surpass the potential limitations of traditional training approaches in facilitating interactive engagement among students. However, research on embodied virtual assistants that leverage generative artificial intelligence (AI) and verbal communication in the anatomy education context is underrepresented. In this work, we introduce a VR environment with a generative AI-embodied virtual assistant to support participants in responding to varying cognitive complexity anatomy questions and enable verbal communication. We assessed the technical efficacy and usability of the proposed environment in a pilot user study with 16 participants. We conducted a within-subject design for virtual assistant configuration (avatar- and screen-based), with two levels of cognitive complexity (knowledge- and analysis-based). The results reveal a significant difference in the scores obtained from knowledge- and analysis-based questions in relation to avatar configuration. Moreover, results provide insights into usability, cognitive task load, and the sense of presence in the proposed virtual assistant configurations. Our environment and results of the pilot study offer potential benefits and future research directions beyond medical education, using generative AI and embodied virtual agents as customized virtual conversational assistants. © 2024 IEEE.
Weid, M.; Khezrian, N.; Mana, A. P.; Farzinnejad, F.; Grubert, J.
GenDeck: Towards a HoloDeck with Text-to-3D Model Generation Proceedings Article
In: Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW, pp. 1188–1189, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037449-0 (ISBN).
Abstract | Links | BibTeX | Tags: 3D content, 3D modeling, 3D models, 3d-modeling, Computational costs, Extende Reality, Human computer interaction, Immersive virtual reality, Knowledge Work, Model generation, Proof of concept, Three dimensional computer graphics, Virtual Reality, Visual fidelity
@inproceedings{weid_gendeck_2024,
title = {GenDeck: Towards a HoloDeck with Text-to-3D Model Generation},
author = {M. Weid and N. Khezrian and A. P. Mana and F. Farzinnejad and J. Grubert},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195600251&doi=10.1109%2fVRW62533.2024.00388&partnerID=40&md5=6dab0cc05259fa2dbe0a2b3806e569af},
doi = {10.1109/VRW62533.2024.00388},
isbn = {979-835037449-0 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},
pages = {1188–1189},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Generative Artificial Intelligence has the potential to substantially transform the way 3D content for Extended Reality applications is produced. Specifically, the development of text-to-3D and image-to-3D generators with increasing visual fidelity and decreasing computational costs is thriving quickly. Within this work, we present GenDeck, a proof-of-concept application to experience text-to-3D model generation inside an immersive Virtual Reality environment. © 2024 IEEE.},
keywords = {3D content, 3D modeling, 3D models, 3d-modeling, Computational costs, Extende Reality, Human computer interaction, Immersive virtual reality, Knowledge Work, Model generation, Proof of concept, Three dimensional computer graphics, Virtual Reality, Visual fidelity},
pubstate = {published},
tppubtype = {inproceedings}
}
Generative Artificial Intelligence has the potential to substantially transform the way 3D content for Extended Reality applications is produced. Specifically, the development of text-to-3D and image-to-3D generators with increasing visual fidelity and decreasing computational costs is thriving quickly. Within this work, we present GenDeck, a proof-of-concept application to experience text-to-3D model generation inside an immersive Virtual Reality environment. © 2024 IEEE.
Schmidt, P.; Arlt, S.; Ruiz-Gonzalez, C.; Gu, X.; Rodríguez, C.; Krenn, M.
Virtual reality for understanding artificial-intelligence-driven scientific discovery with an application in quantum optics Journal Article
In: Machine Learning: Science and Technology, vol. 5, no. 3, 2024, ISSN: 26322153 (ISSN).
Abstract | Links | BibTeX | Tags: 3-dimensional, Analysis process, Digital discovery, Generative adversarial networks, Generative model, generative models, Human capability, Immersive virtual reality, Intelligence models, Quantum entanglement, Quantum optics, Scientific discovery, Scientific understanding, Virtual Reality, Virtual-reality environment
@article{schmidt_virtual_2024,
title = {Virtual reality for understanding artificial-intelligence-driven scientific discovery with an application in quantum optics},
author = {P. Schmidt and S. Arlt and C. Ruiz-Gonzalez and X. Gu and C. Rodríguez and M. Krenn},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201265211&doi=10.1088%2f2632-2153%2fad5fdb&partnerID=40&md5=3a6af280ba0ac81507ade10f5dd1efb3},
doi = {10.1088/2632-2153/ad5fdb},
issn = {26322153 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Machine Learning: Science and Technology},
volume = {5},
number = {3},
abstract = {Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive virtual reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way—as a human-in-the-loop—to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR to enhance a researcher’s ability to derive knowledge from graph-based generative AI. This type of AI is a widely used abstract data representation in various scientific fields. © 2024 The Author(s). Published by IOP Publishing Ltd.},
keywords = {3-dimensional, Analysis process, Digital discovery, Generative adversarial networks, Generative model, generative models, Human capability, Immersive virtual reality, Intelligence models, Quantum entanglement, Quantum optics, Scientific discovery, Scientific understanding, Virtual Reality, Virtual-reality environment},
pubstate = {published},
tppubtype = {article}
}
Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive virtual reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way—as a human-in-the-loop—to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR to enhance a researcher’s ability to derive knowledge from graph-based generative AI. This type of AI is a widely used abstract data representation in various scientific fields. © 2024 The Author(s). Published by IOP Publishing Ltd.