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).
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}
}
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.
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.