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
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), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
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=fc4deb58acaf5bac8f4805ef7035396d},
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. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
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}
}
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. © 2025 Elsevier B.V., All rights reserved.
Chen, Y.; Yan, Y.; Yang, G.
Bringing Microbiology to Life in Museum: Using Mobile VR and LLM-Powered Virtual Character for Children's Science Learning Proceedings Article
In: Chui, K. T.; Jaikaeo, C.; Niramitranon, J.; Kaewmanee, W.; Ng, K. -K.; Ongkunaruk, P. (Ed.): pp. 83–87, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331595500 (ISBN).
Abstract | Links | BibTeX | Tags: Computer aided instruction, E-Learning, Engineering education, Experimental groups, Immersive technologies, Informal learning, Language Model, Large language model, large language models, Learning systems, Microbiology, Mobile virtual reality, Museum, Museums, Science education, Science learning, Virtual addresses, Virtual character, Virtual Reality, Virtual reality system
@inproceedings{chen_bringing_2025,
title = {Bringing Microbiology to Life in Museum: Using Mobile VR and LLM-Powered Virtual Character for Children's Science Learning},
author = {Y. Chen and Y. Yan and G. Yang},
editor = {K. T. Chui and C. Jaikaeo and J. Niramitranon and W. Kaewmanee and K. -K. Ng and P. Ongkunaruk},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015708152&doi=10.1109%2FISET65607.2025.00025&partnerID=40&md5=77ae9a4829656155010abc280a817a72},
doi = {10.1109/ISET65607.2025.00025},
isbn = {9798331595500 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {83–87},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Although the increasing advantages of immersive technology-enhanced museum informal learning in children's science education, the application of mobile virtual reality (MVR) technology combined with large language models (LLM) in this environment has not yet been fully explored. Furthermore, virtual character, as an intelligent learning assistant, is capable of providing personalized guidance and instant feedback to children through natural language interactions, but its potential in museum learning has yet to be fully tapped. To address these gaps, this study investigates the effectiveness of integrating MVR with LLM-powered virtual character in promoting children's microbiology learning during museum activities. In this paper, the technology-enhanced POE (Prediction-observation-explanation) learning model was studied, and the corresponding MVR system was designed and developed to carry out microbial learning activities. A quasiexperimental design was used with 60 children aged 10-12. The experimental group learned via an MVR system combining LLM-powered virtual character, while the control group used traditional methods. Results showed the experimental group significantly outperformed the control group in both academic achievement and learning motivation, including attention, confidence, and satisfaction. This provides evidence for using immersive technologies in informal learning and offers insights into applying LLM-powered virtual character in science education. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Computer aided instruction, E-Learning, Engineering education, Experimental groups, Immersive technologies, Informal learning, Language Model, Large language model, large language models, Learning systems, Microbiology, Mobile virtual reality, Museum, Museums, Science education, Science learning, Virtual addresses, Virtual character, Virtual Reality, Virtual reality system},
pubstate = {published},
tppubtype = {inproceedings}
}
Although the increasing advantages of immersive technology-enhanced museum informal learning in children's science education, the application of mobile virtual reality (MVR) technology combined with large language models (LLM) in this environment has not yet been fully explored. Furthermore, virtual character, as an intelligent learning assistant, is capable of providing personalized guidance and instant feedback to children through natural language interactions, but its potential in museum learning has yet to be fully tapped. To address these gaps, this study investigates the effectiveness of integrating MVR with LLM-powered virtual character in promoting children's microbiology learning during museum activities. In this paper, the technology-enhanced POE (Prediction-observation-explanation) learning model was studied, and the corresponding MVR system was designed and developed to carry out microbial learning activities. A quasiexperimental design was used with 60 children aged 10-12. The experimental group learned via an MVR system combining LLM-powered virtual character, while the control group used traditional methods. Results showed the experimental group significantly outperformed the control group in both academic achievement and learning motivation, including attention, confidence, and satisfaction. This provides evidence for using immersive technologies in informal learning and offers insights into applying LLM-powered virtual character in science education. © 2025 Elsevier B.V., All rights reserved.