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
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.
2024
Fostering Personalized Learning in Data Science: Integrating Innovative Tools and Strategies for Diverse Pathways Proceedings Article
In: IEEE Int. Conf. Eng. Educ.: Dissem. Adv. Eng. Educ. using Artif. Intell., ICEED, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835036741-6 (ISBN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, ChatGPT-4, Content recommendation, Content recommendations, Contrastive Learning, Data Science, Data science education, Federated learning, Individualized learning, Individualized learning experience framework, Learning experiences, Prerequisite skill identification, Science education, Self-directed learning, Teaching approaches, Virtual environments, Virtual Reality
@inproceedings{noauthor_fostering_2024,
title = {Fostering Personalized Learning in Data Science: Integrating Innovative Tools and Strategies for Diverse Pathways},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001849041&doi=10.1109%2fICEED62316.2024.10923798&partnerID=40&md5=cfec507f601df5ffc3b07db0df6d80a7},
doi = {10.1109/ICEED62316.2024.10923798},
isbn = {979-835036741-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Int. Conf. Eng. Educ.: Dissem. Adv. Eng. Educ. using Artif. Intell., ICEED},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This paper introduces an innovative teaching approach in data science tailored for students in non-computer science pathways, specifically Business Information Technology (BIT) and Computing and Information Technology (CIT). Over a five-year period, a unique teaching approach has been developed incorporating a virtual reality (VR) game event and ChatGPT-4 as a generative artificial intelligence (AI) tool. To address the inherent complexities of learning data science, particularly the diverse prerequisite skills, this study introduces a framework including a diagnostic assessment centered around a specific education research question: 'How can the learning experiences of individual students be customized to address the multifaceted challenges of data science education?' Through a diagnostic assessment process, conducted via a survey completed by students, this framework identifies students' unique requirements and skill areas facilitating the delivery of personalized content recommendations within the initial week of teaching. By fostering a culture of self-directed learning, the approach aims to enable students to concentrate on essential customized learning materials. This paper also highlights the overall student satisfaction with the module averaged 4.5 out of 5 with a standard deviation of 0.9 indicating a high level of contentment with the teaching approach. The discussion encompasses the framework's implications for teaching and its alignment with educational theories. This paper contributes to the computing education field by addressing the research question and offering insights for future research and teaching practices. © 2024 IEEE.},
keywords = {Adversarial machine learning, ChatGPT-4, Content recommendation, Content recommendations, Contrastive Learning, Data Science, Data science education, Federated learning, Individualized learning, Individualized learning experience framework, Learning experiences, Prerequisite skill identification, Science education, Self-directed learning, Teaching approaches, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper introduces an innovative teaching approach in data science tailored for students in non-computer science pathways, specifically Business Information Technology (BIT) and Computing and Information Technology (CIT). Over a five-year period, a unique teaching approach has been developed incorporating a virtual reality (VR) game event and ChatGPT-4 as a generative artificial intelligence (AI) tool. To address the inherent complexities of learning data science, particularly the diverse prerequisite skills, this study introduces a framework including a diagnostic assessment centered around a specific education research question: 'How can the learning experiences of individual students be customized to address the multifaceted challenges of data science education?' Through a diagnostic assessment process, conducted via a survey completed by students, this framework identifies students' unique requirements and skill areas facilitating the delivery of personalized content recommendations within the initial week of teaching. By fostering a culture of self-directed learning, the approach aims to enable students to concentrate on essential customized learning materials. This paper also highlights the overall student satisfaction with the module averaged 4.5 out of 5 with a standard deviation of 0.9 indicating a high level of contentment with the teaching approach. The discussion encompasses the framework's implications for teaching and its alignment with educational theories. This paper contributes to the computing education field by addressing the research question and offering insights for future research and teaching practices. © 2024 IEEE.