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
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.
2023
Horvath, I.; Csapo, A. B.
Structured Template Language and Generative AI Driven Content Management for Personalized Workspace Reconfiguration Proceedings Article
In: IEEE Int. Conf. Cogn. Asp. Virtual Real., CVR, Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 979-835033863-8 (ISBN).
Abstract | Links | BibTeX | Tags: 3D spaces, 3D virtual reality, Cognitive infocommunications, Content management, Content management solutions, Geometric layout, Knowledge engineering, Semantic content, Semantic content management, Semantics, Simple++, Virtual Reality, Work-flows
@inproceedings{horvath_structured_2023,
title = {Structured Template Language and Generative AI Driven Content Management for Personalized Workspace Reconfiguration},
author = {I. Horvath and A. B. Csapo},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184849596&doi=10.1109%2fCVR58941.2023.10395520&partnerID=40&md5=c3890e80798c9ec542fe453875dde253},
doi = {10.1109/CVR58941.2023.10395520},
isbn = {979-835033863-8 (ISBN)},
year = {2023},
date = {2023-01-01},
booktitle = {IEEE Int. Conf. Cogn. Asp. Virtual Real., CVR},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This work presents a systematic approach towards personalized workspace management and reconfiguration in 3D Virtual Reality (VR) spaces, focusing on a structured template language for defining and manipulating content layout schemas, as well as a generative AI supported content management solution. Recognizing the varying requirements of different tasks and workflows, on the one hand we propose a hierarchical template language that enables, through simple steps, the adaptation of number and variety of documents within geometric layout schemas in digital 3D spaces. In the second half of the paper, we present a generative AI driven framework for integrating associative-semantic content management into such workspaces, thereby enhancing the relevance and contextuality of workspace configurations. The proposed approach aids in identifying content that is semantically linked to a given spatial, temporal and topical environment, enabling workspace designers and users to create more efficient and personalized workspace layouts. © 2023 IEEE.},
keywords = {3D spaces, 3D virtual reality, Cognitive infocommunications, Content management, Content management solutions, Geometric layout, Knowledge engineering, Semantic content, Semantic content management, Semantics, Simple++, Virtual Reality, Work-flows},
pubstate = {published},
tppubtype = {inproceedings}
}
This work presents a systematic approach towards personalized workspace management and reconfiguration in 3D Virtual Reality (VR) spaces, focusing on a structured template language for defining and manipulating content layout schemas, as well as a generative AI supported content management solution. Recognizing the varying requirements of different tasks and workflows, on the one hand we propose a hierarchical template language that enables, through simple steps, the adaptation of number and variety of documents within geometric layout schemas in digital 3D spaces. In the second half of the paper, we present a generative AI driven framework for integrating associative-semantic content management into such workspaces, thereby enhancing the relevance and contextuality of workspace configurations. The proposed approach aids in identifying content that is semantically linked to a given spatial, temporal and topical environment, enabling workspace designers and users to create more efficient and personalized workspace layouts. © 2023 IEEE.