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
Weerasinghe, K.; Janapati, S.; Ge, X.; Kim, S.; Iyer, S.; Stankovic, J. A.; Alemzadeh, H.
Real-Time Multimodal Cognitive Assistant for Emergency Medical Services Proceedings Article
In: Proc. - ACM/IEEE Conf. Internet-of-Things Des. Implement., IoTDI, pp. 85–96, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835037025-6 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Cognitive Assistance, Computational Linguistics, Decision making, Domain knowledge, Edge computing, Emergency medical services, Forecasting, Graphic methods, Language Model, machine learning, Machine-learning, Multi-modal, Real- time, Service protocols, Smart Health, Speech recognition, State of the art
@inproceedings{weerasinghe_real-time_2024,
title = {Real-Time Multimodal Cognitive Assistant for Emergency Medical Services},
author = {K. Weerasinghe and S. Janapati and X. Ge and S. Kim and S. Iyer and J. A. Stankovic and H. Alemzadeh},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197769304&doi=10.1109%2fIoTDI61053.2024.00012&partnerID=40&md5=a3b7cf14e46ecb2d4e49905fb845f2c9},
doi = {10.1109/IoTDI61053.2024.00012},
isbn = {979-835037025-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - ACM/IEEE Conf. Internet-of-Things Des. Implement., IoTDI},
pages = {85–96},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thinking and rapid decision-making. This paper presents CognitiveEMS, an end-to-end wearable cognitive assistant system that can act as a collaborative virtual partner engaging in the real-time acquisition and analysis of multimodal data from an emergency scene and interacting with EMS responders through Augmented Reality (AR) smart glasses. CognitiveEMS processes the continuous streams of data in real-time and leverages edge computing to provide assistance in EMS protocol selection and intervention recognition. We address key technical challenges in real-time cognitive assistance by introducing three novel components: (i) a Speech Recognition model that is fine-tuned for real-world medical emergency conversations using simulated EMS audio recordings, augmented with synthetic data generated by large language models (LLMs); (ii) an EMS Protocol Prediction model that combines state-of-the-art (SOTA) tiny language models with EMS domain knowledge using graph-based attention mechanisms; (iii) an EMS Action Recognition module which leverages multimodal audio and video data and protocol predictions to infer the intervention/treatment actions taken by the responders at the incident scene. Our results show that for speech recognition we achieve superior performance compared to SOTA (WER of 0.290 vs. 0.618) on conversational data. Our protocol prediction component also significantly outperforms SOTA (top-3 accuracy of 0.800 vs. 0.200) and the action recognition achieves an accuracy of 0.727, while maintaining an end-to-end latency of 3.78s for protocol prediction on the edge and 0.31s on the server. © 2024 IEEE.},
keywords = {Artificial intelligence, Augmented Reality, Cognitive Assistance, Computational Linguistics, Decision making, Domain knowledge, Edge computing, Emergency medical services, Forecasting, Graphic methods, Language Model, machine learning, Machine-learning, Multi-modal, Real- time, Service protocols, Smart Health, Speech recognition, State of the art},
pubstate = {published},
tppubtype = {inproceedings}
}
Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thinking and rapid decision-making. This paper presents CognitiveEMS, an end-to-end wearable cognitive assistant system that can act as a collaborative virtual partner engaging in the real-time acquisition and analysis of multimodal data from an emergency scene and interacting with EMS responders through Augmented Reality (AR) smart glasses. CognitiveEMS processes the continuous streams of data in real-time and leverages edge computing to provide assistance in EMS protocol selection and intervention recognition. We address key technical challenges in real-time cognitive assistance by introducing three novel components: (i) a Speech Recognition model that is fine-tuned for real-world medical emergency conversations using simulated EMS audio recordings, augmented with synthetic data generated by large language models (LLMs); (ii) an EMS Protocol Prediction model that combines state-of-the-art (SOTA) tiny language models with EMS domain knowledge using graph-based attention mechanisms; (iii) an EMS Action Recognition module which leverages multimodal audio and video data and protocol predictions to infer the intervention/treatment actions taken by the responders at the incident scene. Our results show that for speech recognition we achieve superior performance compared to SOTA (WER of 0.290 vs. 0.618) on conversational data. Our protocol prediction component also significantly outperforms SOTA (top-3 accuracy of 0.800 vs. 0.200) and the action recognition achieves an accuracy of 0.727, while maintaining an end-to-end latency of 3.78s for protocol prediction on the edge and 0.31s on the server. © 2024 IEEE.
2021
Cossentino, Massimo; Lopes, Salvatore; Sabatucci, Luca; Tripiciano, Mario
Towards a Semantic Layer for Italian Emergency Plans. Proceedings Article
In: WOA, pp. 144–161, 2021.
Abstract | BibTeX | Tags: Domain knowledge, Emergency plans, Semantic layer, Text to formal conversion
@inproceedings{cossentinoSemanticLayerItalian2021,
title = {Towards a Semantic Layer for Italian Emergency Plans.},
author = { Massimo Cossentino and Salvatore Lopes and Luca Sabatucci and Mario Tripiciano},
year = {2021},
date = {2021-01-01},
booktitle = {WOA},
pages = {144--161},
abstract = {Emergency plans require a complex collaboration among multiple departments and roles. They are generally long textual documents containing practical instructions for hazard responses in natural language. This work focuses on converting informal documents to a more rigorous structured-text representation by taking advantage of well-known techniques from the literature. However, this task is costly, it requires technical skills and sound domain knowledge, and it is entirely subjective. To this aim, we propose a semantic layer that supports the formalization of an emergency plan by identifying essential elements of the input document and highlighting inconsistencies, redundancies, and ambiguities. textcopyright 2021 CEUR-WS. All rights reserved.},
keywords = {Domain knowledge, Emergency plans, Semantic layer, Text to formal conversion},
pubstate = {published},
tppubtype = {inproceedings}
}
Emergency plans require a complex collaboration among multiple departments and roles. They are generally long textual documents containing practical instructions for hazard responses in natural language. This work focuses on converting informal documents to a more rigorous structured-text representation by taking advantage of well-known techniques from the literature. However, this task is costly, it requires technical skills and sound domain knowledge, and it is entirely subjective. To this aim, we propose a semantic layer that supports the formalization of an emergency plan by identifying essential elements of the input document and highlighting inconsistencies, redundancies, and ambiguities. textcopyright 2021 CEUR-WS. All rights reserved.
Cossentino, Massimo; Lopes, Salvatore; Sabatucci, Luca; Tripiciano, Mario
Towards a Semantic Layer for Italian Emergency Plans. Proceedings Article
In: WOA, pp. 144–161, 2021.
Abstract | Links | BibTeX | Tags: Domain knowledge, Emergency plans, Semantic layer, Text to formal conversion
@inproceedings{cossentino_towards_2021,
title = {Towards a Semantic Layer for Italian Emergency Plans.},
author = {Massimo Cossentino and Salvatore Lopes and Luca Sabatucci and Mario Tripiciano},
url = {https://ceur-ws.org/Vol-2963/paper9.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {WOA},
pages = {144–161},
abstract = {Emergency plans require a complex collaboration among multiple departments and roles. They are generally long textual documents containing practical instructions for hazard responses in natural language. This work focuses on converting informal documents to a more rigorous structured-text representation by taking advantage of well-known techniques from the literature. However, this task is costly, it requires technical skills and sound domain knowledge, and it is entirely subjective. To this aim, we propose a semantic layer that supports the formalization of an emergency plan by identifying essential elements of the input document and highlighting inconsistencies, redundancies, and ambiguities. © 2021 CEUR-WS. All rights reserved.},
keywords = {Domain knowledge, Emergency plans, Semantic layer, Text to formal conversion},
pubstate = {published},
tppubtype = {inproceedings}
}
Emergency plans require a complex collaboration among multiple departments and roles. They are generally long textual documents containing practical instructions for hazard responses in natural language. This work focuses on converting informal documents to a more rigorous structured-text representation by taking advantage of well-known techniques from the literature. However, this task is costly, it requires technical skills and sound domain knowledge, and it is entirely subjective. To this aim, we propose a semantic layer that supports the formalization of an emergency plan by identifying essential elements of the input document and highlighting inconsistencies, redundancies, and ambiguities. © 2021 CEUR-WS. All rights reserved.