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
Tracy, K.; Spantidi, O.
Impact of GPT-Driven Teaching Assistants in VR Learning Environments Journal Article
In: IEEE Transactions on Learning Technologies, vol. 18, pp. 192–205, 2025, ISSN: 19391382 (ISSN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Cognitive loads, Computer interaction, Contrastive Learning, Control groups, Experimental groups, Federated learning, Generative AI, Generative artificial intelligence (GenAI), human–computer interaction, Interactive learning environment, interactive learning environments, Learning efficacy, Learning outcome, learning outcomes, Student engagement, Teaching assistants, Virtual environments, Virtual Reality (VR)
@article{tracy_impact_2025,
title = {Impact of GPT-Driven Teaching Assistants in VR Learning Environments},
author = {K. Tracy and O. Spantidi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001083336&doi=10.1109%2fTLT.2025.3539179&partnerID=40&md5=34fea4ea8517a061fe83b8294e1a9a87},
doi = {10.1109/TLT.2025.3539179},
issn = {19391382 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Learning Technologies},
volume = {18},
pages = {192–205},
abstract = {Virtual reality (VR) has emerged as a transformative educational tool, enabling immersive learning environments that promote student engagement and understanding of complex concepts. However, despite the growing adoption of VR in education, there remains a significant gap in research exploring how generative artificial intelligence (AI), such as generative pretrained transformer can further enhance these experiences by reducing cognitive load and improving learning outcomes. This study examines the impact of an AI-driven instructor assistant in VR classrooms on student engagement, cognitive load, knowledge retention, and performance. A total of 52 participants were divided into two groups experiencing a VR lesson on the bubble sort algorithm, one with only a prescripted virtual instructor (control group), and the other with the addition of an AI instructor assistant (experimental group). Statistical analysis of postlesson quizzes and cognitive load assessments was conducted using independent t-tests and analysis of variance (ANOVA), with the cognitive load being measured through a postexperiment questionnaire. The study results indicate that the experimental group reported significantly higher engagement compared to the control group. While the AI assistant did not significantly improve postlesson assessment scores, it enhanced conceptual knowledge transfer. The experimental group also demonstrated lower intrinsic cognitive load, suggesting the assistant reduced the perceived complexity of the material. Higher germane and general cognitive loads indicated that students were more invested in meaningful learning without feeling overwhelmed. © 2008-2011 IEEE.},
keywords = {Adversarial machine learning, Cognitive loads, Computer interaction, Contrastive Learning, Control groups, Experimental groups, Federated learning, Generative AI, Generative artificial intelligence (GenAI), human–computer interaction, Interactive learning environment, interactive learning environments, Learning efficacy, Learning outcome, learning outcomes, Student engagement, Teaching assistants, Virtual environments, Virtual Reality (VR)},
pubstate = {published},
tppubtype = {article}
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Virtual reality (VR) has emerged as a transformative educational tool, enabling immersive learning environments that promote student engagement and understanding of complex concepts. However, despite the growing adoption of VR in education, there remains a significant gap in research exploring how generative artificial intelligence (AI), such as generative pretrained transformer can further enhance these experiences by reducing cognitive load and improving learning outcomes. This study examines the impact of an AI-driven instructor assistant in VR classrooms on student engagement, cognitive load, knowledge retention, and performance. A total of 52 participants were divided into two groups experiencing a VR lesson on the bubble sort algorithm, one with only a prescripted virtual instructor (control group), and the other with the addition of an AI instructor assistant (experimental group). Statistical analysis of postlesson quizzes and cognitive load assessments was conducted using independent t-tests and analysis of variance (ANOVA), with the cognitive load being measured through a postexperiment questionnaire. The study results indicate that the experimental group reported significantly higher engagement compared to the control group. While the AI assistant did not significantly improve postlesson assessment scores, it enhanced conceptual knowledge transfer. The experimental group also demonstrated lower intrinsic cognitive load, suggesting the assistant reduced the perceived complexity of the material. Higher germane and general cognitive loads indicated that students were more invested in meaningful learning without feeling overwhelmed. © 2008-2011 IEEE.
Tsai, Y. -J.; Liu, S. -T.; Hsu, S. -C.
The Development of an Interactive IoT Cross-Media Survey System and Real-Time Re-presentation of Mass Learning Proceedings Article
In: J., Wei; G., Margetis (Ed.): Lect. Notes Comput. Sci., pp. 145–157, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303193060-7 (ISBN).
Abstract | Links | BibTeX | Tags: Cross-media, Data Re-presentation, Internet of Things, IoT Cross-Media System, IoT cross-medium system, Learning outcome, Learning systems, Mass Learning, Media systems, Smart phones, Smartphone, Smartphones, STEM with A, Survey System, Survey systems, Surveying, Tangible User Interface, Tangible user interfaces, User interfaces, Virtual Reality
@inproceedings{tsai_development_2025,
title = {The Development of an Interactive IoT Cross-Media Survey System and Real-Time Re-presentation of Mass Learning},
author = {Y. -J. Tsai and S. -T. Liu and S. -C. Hsu},
editor = {Wei J. and Margetis G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105008756188&doi=10.1007%2f978-3-031-93061-4_10&partnerID=40&md5=c487828eeacfdf18cf4e726e6ce28146},
doi = {10.1007/978-3-031-93061-4_10},
isbn = {03029743 (ISSN); 978-303193060-7 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15823 LNCS},
pages = {145–157},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In this study, we propose the Interactive IoT Cross-Media Survey System, integrating tangible interaction in a game-like manner with real-time data re-presentation. This system was implemented in the “STEM with A” Interactive Exploration Hall at National Tsing Hua University in 2020. It enabled participants to use their smartphones as tangible user interfaces to “scoop-up questions” from interactive sensing points within the exhibition areas. After completing the questions, participants could “pour-in” their responses and observe digital data re-presentation artworks generated from survey results, showcasing mass learning outcomes. Furthermore, the data re-presentation content was tailored to participants’ group characteristics, showing how their responses impact the group’s overall learning outcomes with each “pour-in response.” The study achieved several key outcomes: (1) transforming traditional surveys into a gamified survey system, enhancing participants’ engagement, (2) providing real-time, group-based data re-presentations, enabling participants to contribute to the group’s learning outcomes, and (3) implementing a grouping mechanism to foster collaboration within groups and healthy competition between them. This system provides flexible and customizable data re-presentation, making it suitable for diverse environments requiring real-time data-driven engagement. Future applications can integrate emerging technologies, such as generative AI to dynamically generate questions or virtual reality to offer immersive experiences. Additionally, data re-presentations can be designed as dynamic mass artistic creations, allowing participants to become co-creators of an evolving collective masterpiece. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Cross-media, Data Re-presentation, Internet of Things, IoT Cross-Media System, IoT cross-medium system, Learning outcome, Learning systems, Mass Learning, Media systems, Smart phones, Smartphone, Smartphones, STEM with A, Survey System, Survey systems, Surveying, Tangible User Interface, Tangible user interfaces, User interfaces, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
In this study, we propose the Interactive IoT Cross-Media Survey System, integrating tangible interaction in a game-like manner with real-time data re-presentation. This system was implemented in the “STEM with A” Interactive Exploration Hall at National Tsing Hua University in 2020. It enabled participants to use their smartphones as tangible user interfaces to “scoop-up questions” from interactive sensing points within the exhibition areas. After completing the questions, participants could “pour-in” their responses and observe digital data re-presentation artworks generated from survey results, showcasing mass learning outcomes. Furthermore, the data re-presentation content was tailored to participants’ group characteristics, showing how their responses impact the group’s overall learning outcomes with each “pour-in response.” The study achieved several key outcomes: (1) transforming traditional surveys into a gamified survey system, enhancing participants’ engagement, (2) providing real-time, group-based data re-presentations, enabling participants to contribute to the group’s learning outcomes, and (3) implementing a grouping mechanism to foster collaboration within groups and healthy competition between them. This system provides flexible and customizable data re-presentation, making it suitable for diverse environments requiring real-time data-driven engagement. Future applications can integrate emerging technologies, such as generative AI to dynamically generate questions or virtual reality to offer immersive experiences. Additionally, data re-presentations can be designed as dynamic mass artistic creations, allowing participants to become co-creators of an evolving collective masterpiece. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Lau, K. H. C.; Bozkir, E.; Gao, H.; Kasneci, E.
Evaluating Usability and Engagement of Large Language Models in Virtual Reality for Traditional Scottish Curling Proceedings Article
In: A., Del Bue; C., Canton; J., Pont-Tuset; T., Tommasi (Ed.): Lect. Notes Comput. Sci., pp. 177–195, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303191571-0 (ISBN).
Abstract | Links | BibTeX | Tags: Chatbots, Cultural heritages, Digital Cultural Heritage, Digital cultural heritages, Educational robots, Engineering education, Heritage education, Historic Preservation, Language Model, Large language model, large language models, Learning outcome, Model-based OPC, Usability engineering, User Engagement, Virtual Reality, Virtual-reality environment, Virtualization
@inproceedings{lau_evaluating_2025,
title = {Evaluating Usability and Engagement of Large Language Models in Virtual Reality for Traditional Scottish Curling},
author = {K. H. C. Lau and E. Bozkir and H. Gao and E. Kasneci},
editor = {Del Bue A. and Canton C. and Pont-Tuset J. and Tommasi T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105006905979&doi=10.1007%2f978-3-031-91572-7_11&partnerID=40&md5=8a81fb09ff54e57b9429660a8898149a},
doi = {10.1007/978-3-031-91572-7_11},
isbn = {03029743 (ISSN); 978-303191571-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15628 LNCS},
pages = {177–195},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This paper explores the innovative application of Large Language Models (LLMs) in Virtual Reality (VR) environments to promote heritage education, focusing on traditional Scottish curling presented in the game “Scottish Bonspiel VR”. Our study compares the effectiveness of LLM-based chatbots with pre-defined scripted chatbots, evaluating key criteria such as usability, user engagement, and learning outcomes. The results show that LLM-based chatbots significantly improve interactivity and engagement, creating a more dynamic and immersive learning environment. This integration helps document and preserve cultural heritage and enhances dissemination processes, which are crucial for safeguarding intangible cultural heritage (ICH) amid environmental changes. Furthermore, the study highlights the potential of novel technologies in education to provide immersive experiences that foster a deeper appreciation of cultural heritage. These findings support the wider application of LLMs and VR in cultural education to address global challenges and promote sustainable practices to preserve and enhance cultural heritage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Chatbots, Cultural heritages, Digital Cultural Heritage, Digital cultural heritages, Educational robots, Engineering education, Heritage education, Historic Preservation, Language Model, Large language model, large language models, Learning outcome, Model-based OPC, Usability engineering, User Engagement, Virtual Reality, Virtual-reality environment, Virtualization},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper explores the innovative application of Large Language Models (LLMs) in Virtual Reality (VR) environments to promote heritage education, focusing on traditional Scottish curling presented in the game “Scottish Bonspiel VR”. Our study compares the effectiveness of LLM-based chatbots with pre-defined scripted chatbots, evaluating key criteria such as usability, user engagement, and learning outcomes. The results show that LLM-based chatbots significantly improve interactivity and engagement, creating a more dynamic and immersive learning environment. This integration helps document and preserve cultural heritage and enhances dissemination processes, which are crucial for safeguarding intangible cultural heritage (ICH) amid environmental changes. Furthermore, the study highlights the potential of novel technologies in education to provide immersive experiences that foster a deeper appreciation of cultural heritage. These findings support the wider application of LLMs and VR in cultural education to address global challenges and promote sustainable practices to preserve and enhance cultural heritage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
2024
Gao, H.; Huai, H.; Yildiz-Degirmenci, S.; Bannert, M.; Kasneci, E.
DataliVR: Transformation of Data Literacy Education through Virtual Reality with ChatGPT-Powered Enhancements Proceedings Article
In: U., Eck; M., Sra; J., Stefanucci; M., Sugimoto; M., Tatzgern; I., Williams (Ed.): Proc. - IEEE Int. Symp. Mixed Augment. Real., ISMAR, pp. 120–129, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833151647-5 (ISBN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Chatbots, ChatGPT, Contrastive Learning, Data driven, Data literacy, Digital transformation, Federated learning, Immersive learning, Language Model, Large language model, Learning experiences, Learning outcome, LLMs, Virtual environments, Virtual Reality
@inproceedings{gao_datalivr_2024,
title = {DataliVR: Transformation of Data Literacy Education through Virtual Reality with ChatGPT-Powered Enhancements},
author = {H. Gao and H. Huai and S. Yildiz-Degirmenci and M. Bannert and E. Kasneci},
editor = {Eck U. and Sra M. and Stefanucci J. and Sugimoto M. and Tatzgern M. and Williams I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213525613&doi=10.1109%2fISMAR62088.2024.00026&partnerID=40&md5=abdeba7ecfecc8b1d715d633a29bd11d},
doi = {10.1109/ISMAR62088.2024.00026},
isbn = {979-833151647-5 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real., ISMAR},
pages = {120–129},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Data literacy is essential in today's data-driven world, emphasizing individuals' abilities to effectively manage data and extract meaningful insights. However, traditional classroom-based educational approaches often struggle to fully address the multifaceted nature of data literacy. As education undergoes digital transformation, innovative technologies such as Virtual Reality (VR) offer promising avenues for immersive and engaging learning experiences. This paper introduces DataliVR, a pioneering VR application aimed at enhancing the data literacy skills of university students within a contextual and gamified virtual learning environment. By integrating Large Language Models (LLMs) like ChatGPT as a conversational artificial intelligence (AI) chatbot embodied within a virtual avatar, DataliVR provides personalized learning assistance, enriching user learning experiences. Our study employed an experimental approach, with chatbot availability as the independent variable, analyzing learning experiences and outcomes as dependent variables with a sample of thirty participants. Our approach underscores the effectiveness and user-friendliness of ChatGPT-powered DataliVR in fostering data literacy skills. Moreover, our study examines the impact of the ChatGPT-based AI chatbot on users' learning, revealing significant effects on both learning experiences and outcomes. Our study presents a robust tool for fostering data literacy skills, contributing significantly to the digital advancement of data literacy education through cutting-edge VR and AI technologies. Moreover, our research provides valuable insights and implications for future research endeavors aiming to integrate LLMs (e.g., ChatGPT) into educational VR platforms. © 2024 IEEE.},
keywords = {Adversarial machine learning, Chatbots, ChatGPT, Contrastive Learning, Data driven, Data literacy, Digital transformation, Federated learning, Immersive learning, Language Model, Large language model, Learning experiences, Learning outcome, LLMs, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Data literacy is essential in today's data-driven world, emphasizing individuals' abilities to effectively manage data and extract meaningful insights. However, traditional classroom-based educational approaches often struggle to fully address the multifaceted nature of data literacy. As education undergoes digital transformation, innovative technologies such as Virtual Reality (VR) offer promising avenues for immersive and engaging learning experiences. This paper introduces DataliVR, a pioneering VR application aimed at enhancing the data literacy skills of university students within a contextual and gamified virtual learning environment. By integrating Large Language Models (LLMs) like ChatGPT as a conversational artificial intelligence (AI) chatbot embodied within a virtual avatar, DataliVR provides personalized learning assistance, enriching user learning experiences. Our study employed an experimental approach, with chatbot availability as the independent variable, analyzing learning experiences and outcomes as dependent variables with a sample of thirty participants. Our approach underscores the effectiveness and user-friendliness of ChatGPT-powered DataliVR in fostering data literacy skills. Moreover, our study examines the impact of the ChatGPT-based AI chatbot on users' learning, revealing significant effects on both learning experiences and outcomes. Our study presents a robust tool for fostering data literacy skills, contributing significantly to the digital advancement of data literacy education through cutting-edge VR and AI technologies. Moreover, our research provides valuable insights and implications for future research endeavors aiming to integrate LLMs (e.g., ChatGPT) into educational VR platforms. © 2024 IEEE.