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
Nygren, T.; Samuelsson, M.; Hansson, P. -O.; Efimova, E.; Bachelder, S.
In: International Journal of Artificial Intelligence in Education, 2025, ISSN: 15604292 (ISSN).
Abstract | Links | BibTeX | Tags: AI-generated feedback, Controversial issue in social study education, Controversial issues in social studies education, Curricula, Domain knowledge, Economic and social effects, Expert systems, Generative AI, Human engineering, Knowledge engineering, Language Model, Large language model, large language models (LLMs), Mixed reality, Mixed reality simulation, Mixed reality simulation (MRS), Pedagogical content knowledge, Pedagogical content knowledge (PCK), Personnel training, Preservice teachers, Social studies education, Teacher training, Teacher training simulation, Teacher training simulations, Teaching, Training simulation
@article{nygren_ai_2025,
title = {AI Versus Human Feedback in Mixed Reality Simulations: Comparing LLM and Expert Mentoring in Preservice Teacher Education on Controversial Issues},
author = {T. Nygren and M. Samuelsson and P. -O. Hansson and E. Efimova and S. Bachelder},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007244772&doi=10.1007%2fs40593-025-00484-8&partnerID=40&md5=d3cb14a8117045505cbbeb174b32b88d},
doi = {10.1007/s40593-025-00484-8},
issn = {15604292 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Artificial Intelligence in Education},
abstract = {This study explores the potential role of AI-generated mentoring within simulated environments designed for teacher education, specifically focused on the challenges of teaching controversial issues. Using a mixed-methods approach, we empirically investigate the potential and challenges of AI-generated feedback compared to that provided by human experts when mentoring preservice teachers in the context of mixed reality simulations. Findings reveal that human experts offered more mixed and nuanced feedback than ChatGPT-4o and Perplexity, especially when identifying missed teaching opportunities and balancing classroom discussions. The AI models evaluated were publicly available pro versions of LLMs and were tested using detailed prompts and coding schemes aligned with educational theories. AI systems were not very good at identifying aspects of general, pedagogical or content knowledge based on Shulman’s theories but were still quite effective in generating feedback in line with human experts. The study highlights the promise of AI to enhance teacher training but underscores the importance of combining AI feedback with expert insights to address the complexities of real-world teaching. This research contributes to a growing understanding of AI's potential role and limitations in education. It suggests that, while AI can be valuable to scale mixed reality simulations, it should be carefully evaluated and balanced by human expertise in teacher education. © The Author(s) 2025.},
keywords = {AI-generated feedback, Controversial issue in social study education, Controversial issues in social studies education, Curricula, Domain knowledge, Economic and social effects, Expert systems, Generative AI, Human engineering, Knowledge engineering, Language Model, Large language model, large language models (LLMs), Mixed reality, Mixed reality simulation, Mixed reality simulation (MRS), Pedagogical content knowledge, Pedagogical content knowledge (PCK), Personnel training, Preservice teachers, Social studies education, Teacher training, Teacher training simulation, Teacher training simulations, Teaching, Training simulation},
pubstate = {published},
tppubtype = {article}
}
This study explores the potential role of AI-generated mentoring within simulated environments designed for teacher education, specifically focused on the challenges of teaching controversial issues. Using a mixed-methods approach, we empirically investigate the potential and challenges of AI-generated feedback compared to that provided by human experts when mentoring preservice teachers in the context of mixed reality simulations. Findings reveal that human experts offered more mixed and nuanced feedback than ChatGPT-4o and Perplexity, especially when identifying missed teaching opportunities and balancing classroom discussions. The AI models evaluated were publicly available pro versions of LLMs and were tested using detailed prompts and coding schemes aligned with educational theories. AI systems were not very good at identifying aspects of general, pedagogical or content knowledge based on Shulman’s theories but were still quite effective in generating feedback in line with human experts. The study highlights the promise of AI to enhance teacher training but underscores the importance of combining AI feedback with expert insights to address the complexities of real-world teaching. This research contributes to a growing understanding of AI's potential role and limitations in education. It suggests that, while AI can be valuable to scale mixed reality simulations, it should be carefully evaluated and balanced by human expertise in teacher education. © The Author(s) 2025.
2024
Asra, S. A.; Wickramarathne, J.
Artificial Intelligence (AI) in Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR) Experiences: Enhancing Immersion and Interaction for User Experiences Proceedings Article
In: B., Luo; S.K., Sahoo; Y.H., Lee; C.H.T., Lee; M., Ong; A., Alphones (Ed.): IEEE Reg 10 Annu Int Conf Proc TENCON, pp. 1700–1705, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 21593442 (ISSN); 979-835035082-1 (ISBN).
Abstract | Links | BibTeX | Tags: AI, AR, Emersion experience, Immersive augmented realities, Mixed reality, MR, Primary sources, Real-world, Secondary sources, Training simulation, Users' experiences, Video game simulation, Video training, Virtual environments, VR
@inproceedings{asra_artificial_2024,
title = {Artificial Intelligence (AI) in Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR) Experiences: Enhancing Immersion and Interaction for User Experiences},
author = {S. A. Asra and J. Wickramarathne},
editor = {Luo B. and Sahoo S.K. and Lee Y.H. and Lee C.H.T. and Ong M. and Alphones A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000443498&doi=10.1109%2fTENCON61640.2024.10902724&partnerID=40&md5=2ff92b5e2529ae7fe797cd8026e8065d},
doi = {10.1109/TENCON61640.2024.10902724},
isbn = {21593442 (ISSN); 979-835035082-1 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Reg 10 Annu Int Conf Proc TENCON},
pages = {1700–1705},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The utilisation of Artificial Intelligence (AI) generated material is one of the most fascinating advancements in the rapidly growing fields of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Two examples of how AI-generated material is revolutionising how we interact with AR, VR and MR are video games and training simulations. In this essay, we'll examine the intriguing potential of AI-generated content and how it's being used to the development of hybrid real-world/virtual experiences. Using this strategy, we acquired the information from primary and secondary sources. We surveyed AR, VR, and MR users to compile the data for the primary source. Then, utilising published papers as a secondary source, information was gathered. By elucidating the concept of context immersion, this research can lay the foundation for the advancement of information regarding immersive AR, VR, and MR contexts. We are able to offer recommendations for overcoming the weak parts and strengthening the good ones based on the questionnaire survey findings. © 2024 IEEE.},
keywords = {AI, AR, Emersion experience, Immersive augmented realities, Mixed reality, MR, Primary sources, Real-world, Secondary sources, Training simulation, Users' experiences, Video game simulation, Video training, Virtual environments, VR},
pubstate = {published},
tppubtype = {inproceedings}
}
The utilisation of Artificial Intelligence (AI) generated material is one of the most fascinating advancements in the rapidly growing fields of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Two examples of how AI-generated material is revolutionising how we interact with AR, VR and MR are video games and training simulations. In this essay, we'll examine the intriguing potential of AI-generated content and how it's being used to the development of hybrid real-world/virtual experiences. Using this strategy, we acquired the information from primary and secondary sources. We surveyed AR, VR, and MR users to compile the data for the primary source. Then, utilising published papers as a secondary source, information was gathered. By elucidating the concept of context immersion, this research can lay the foundation for the advancement of information regarding immersive AR, VR, and MR contexts. We are able to offer recommendations for overcoming the weak parts and strengthening the good ones based on the questionnaire survey findings. © 2024 IEEE.
Kapadia, N.; Gokhale, S.; Nepomuceno, A.; Cheng, W.; Bothwell, S.; Mathews, M.; Shallat, J. S.; Schultz, C.; Gupta, A.
Evaluation of Large Language Model Generated Dialogues for an AI Based VR Nurse Training Simulator Proceedings Article
In: J.Y.C., Chen; G., Fragomeni (Ed.): Lect. Notes Comput. Sci., pp. 200–212, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303161040-0 (ISBN).
Abstract | Links | BibTeX | Tags: Bard, ChatGPT, ClaudeAI, Clinical research, Computational Linguistics, Dialogue Generation, Dialogue generations, Education computing, Extended reality, Health care education, Healthcare Education, Language Model, Language processing, Large language model, large language models, Natural Language Processing, Natural language processing systems, Natural languages, Nurse Training Simulation, Nursing, Patient avatar, Patient Avatars, Semantics, Students, Training simulation, Virtual Reality
@inproceedings{kapadia_evaluation_2024,
title = {Evaluation of Large Language Model Generated Dialogues for an AI Based VR Nurse Training Simulator},
author = {N. Kapadia and S. Gokhale and A. Nepomuceno and W. Cheng and S. Bothwell and M. Mathews and J. S. Shallat and C. Schultz and A. Gupta},
editor = {Chen J.Y.C. and Fragomeni G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196200653&doi=10.1007%2f978-3-031-61041-7_13&partnerID=40&md5=8890a8d0c289fdf6e7ab82e105249097},
doi = {10.1007/978-3-031-61041-7_13},
isbn = {03029743 (ISSN); 978-303161040-0 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {14706 LNCS},
pages = {200–212},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This paper explores the efficacy of Large Language Models (LLMs) in generating dialogues for patient avatars in Virtual Reality (VR) nurse training simulators. With the integration of technology in healthcare education evolving rapidly, the potential of NLP to enhance nurse training through realistic patient interactions presents a significant opportunity. Our study introduces a novel LLM-based dialogue generation system, leveraging models such as ChatGPT, GoogleBard, and ClaudeAI. We detail the development of our script generation system, which was a collaborative endeavor involving nurses, technical artists, and developers. The system, tested on the Meta Quest 2 VR headset, integrates complex dialogues created through a synthesis of clinical expertise and advanced NLP, aimed at simulating real-world nursing scenarios. Through a comprehensive evaluation involving lexical and semantic similarity tests compared to clinical expert-generated scripts, we assess the potential of LLMs as suitable alternatives for script generation. The findings aim to contribute to the development of a more interactive and effective VR nurse training simulator, enhancing communication skills among nursing students for improved patient care outcomes. This research underscores the importance of advanced NLP applications in healthcare education, offering insights into the practicality and limitations of employing LLMs in clinical training environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Bard, ChatGPT, ClaudeAI, Clinical research, Computational Linguistics, Dialogue Generation, Dialogue generations, Education computing, Extended reality, Health care education, Healthcare Education, Language Model, Language processing, Large language model, large language models, Natural Language Processing, Natural language processing systems, Natural languages, Nurse Training Simulation, Nursing, Patient avatar, Patient Avatars, Semantics, Students, Training simulation, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper explores the efficacy of Large Language Models (LLMs) in generating dialogues for patient avatars in Virtual Reality (VR) nurse training simulators. With the integration of technology in healthcare education evolving rapidly, the potential of NLP to enhance nurse training through realistic patient interactions presents a significant opportunity. Our study introduces a novel LLM-based dialogue generation system, leveraging models such as ChatGPT, GoogleBard, and ClaudeAI. We detail the development of our script generation system, which was a collaborative endeavor involving nurses, technical artists, and developers. The system, tested on the Meta Quest 2 VR headset, integrates complex dialogues created through a synthesis of clinical expertise and advanced NLP, aimed at simulating real-world nursing scenarios. Through a comprehensive evaluation involving lexical and semantic similarity tests compared to clinical expert-generated scripts, we assess the potential of LLMs as suitable alternatives for script generation. The findings aim to contribute to the development of a more interactive and effective VR nurse training simulator, enhancing communication skills among nursing students for improved patient care outcomes. This research underscores the importance of advanced NLP applications in healthcare education, offering insights into the practicality and limitations of employing LLMs in clinical training environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.