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
Kadri, M.; Boubakri, F. -E.; Azough, A.; Zidani, K. A.
Game-Based VR Anatomy Learning with Generative AI: Proof of Concept for GenAiVR-Lab Proceedings Article
In: pp. 100–105, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331534899 (ISBN).
Abstract | Links | BibTeX | Tags: Anatomy educations, Artificial intelligence, Bone, Bone fragments, Collaborative learning, E-Learning, Educational Evaluation, Game-Based, Game-based learning, Generative AI, Human computer interaction, Human skeleton, Laboratories, Learning systems, Medical students, Proof of concept, Virtual Reality, Virtual Reality Anatomy
@inproceedings{kadri_game-based_2025,
title = {Game-Based VR Anatomy Learning with Generative AI: Proof of Concept for GenAiVR-Lab},
author = {M. Kadri and F. -E. Boubakri and A. Azough and K. A. Zidani},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015604062&doi=10.1109%2FSCME62582.2025.11104860&partnerID=40&md5=c557ca7975a9683e8c271fbb3a21c4e4},
doi = {10.1109/SCME62582.2025.11104860},
isbn = {9798331534899 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {100–105},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Anatomy education often fails to engage learners or foster precise 3D spatial understanding of complex systems like the human skeleton. We present a Game-Based VR Anatomy Learning system with Generative AI, introduced as a Proof of Concept for our GenAiVR-Lab framework. This prototype validates the foundational pillars of our future development. In the Anatomy Lab scenario, 25 medical students explore a virtual skeleton and undertake a timed mission: assemble three bone fragments within two minutes. Incorrect picks are disabled with point deductions; learners may request a one-shot conversational hint from a ChatGPT-powered Virtual Anatomy Instructor; if time expires, a teammate continues with remaining time. We measured perception changes using pre- and post-test versions of four Perspective Questionnaires: Learning Perspective (LPQ), VR-AI Perspective (VRAIPQ), Generative AI Perspective (GAIPQ), and Game-Based Learning Perspective (GBLPQ). Results demonstrate significant improvements across all four perspectives, with mean scores increasing by approximately 1.3 points on the 5-point Likert scale and nearly all participants showing positive gains. Effect sizes ranged from 2.52 to 3.34, indicating large practical significance, with all measures reaching statistical significance. These findings demonstrate that collaborative game mechanics and generative AI guidance enhance engagement and spatial reasoning. We contrast this PoC with the full GenAiVR-Lab vision - integrating Retrieval-Augmented Generation for precise feedback, multimodal I/O, and adaptive pathways - and outline a roadmap for next-generation immersive anatomy education. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Anatomy educations, Artificial intelligence, Bone, Bone fragments, Collaborative learning, E-Learning, Educational Evaluation, Game-Based, Game-based learning, Generative AI, Human computer interaction, Human skeleton, Laboratories, Learning systems, Medical students, Proof of concept, Virtual Reality, Virtual Reality Anatomy},
pubstate = {published},
tppubtype = {inproceedings}
}
Anatomy education often fails to engage learners or foster precise 3D spatial understanding of complex systems like the human skeleton. We present a Game-Based VR Anatomy Learning system with Generative AI, introduced as a Proof of Concept for our GenAiVR-Lab framework. This prototype validates the foundational pillars of our future development. In the Anatomy Lab scenario, 25 medical students explore a virtual skeleton and undertake a timed mission: assemble three bone fragments within two minutes. Incorrect picks are disabled with point deductions; learners may request a one-shot conversational hint from a ChatGPT-powered Virtual Anatomy Instructor; if time expires, a teammate continues with remaining time. We measured perception changes using pre- and post-test versions of four Perspective Questionnaires: Learning Perspective (LPQ), VR-AI Perspective (VRAIPQ), Generative AI Perspective (GAIPQ), and Game-Based Learning Perspective (GBLPQ). Results demonstrate significant improvements across all four perspectives, with mean scores increasing by approximately 1.3 points on the 5-point Likert scale and nearly all participants showing positive gains. Effect sizes ranged from 2.52 to 3.34, indicating large practical significance, with all measures reaching statistical significance. These findings demonstrate that collaborative game mechanics and generative AI guidance enhance engagement and spatial reasoning. We contrast this PoC with the full GenAiVR-Lab vision - integrating Retrieval-Augmented Generation for precise feedback, multimodal I/O, and adaptive pathways - and outline a roadmap for next-generation immersive anatomy education. © 2025 Elsevier B.V., All rights reserved.
2024
Takata, T.; Yamada, R.; Rene, A. Oliveira Nzinga; Xu, K.; Fujimoto, M.
Development of a Virtual Patient Model for Kampo Medical Interview: New Approach for Enhancing Empathy and Understanding of Kampo Medicine Pathological Concepts Proceedings Article
In: Jt. Int. Conf. Soft Comput. Intell. Syst. Int. Symp. Adv. Intell. Syst., SCIS ISIS, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 9798350373332 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Clinical practices, Clinical training, Complementary and alternative medicines, Covid-19, Diagnosis, Educational approach, Empathy, Kampo medical interview, Medical education, Medical student, Medical students, New approaches, Virtual environments, Virtual patient, Virtual patient models, Virtual patients, Virtual Reality
@inproceedings{takata_development_2024,
title = {Development of a Virtual Patient Model for Kampo Medical Interview: New Approach for Enhancing Empathy and Understanding of Kampo Medicine Pathological Concepts},
author = {T. Takata and R. Yamada and A. Oliveira Nzinga Rene and K. Xu and M. Fujimoto},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214666311&doi=10.1109%2FSCISISIS61014.2024.10759962&partnerID=40&md5=bd7533cb6736986fa2384eb28ded7d0c},
doi = {10.1109/SCISISIS61014.2024.10759962},
isbn = {9798350373332 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Jt. Int. Conf. Soft Comput. Intell. Syst. Int. Symp. Adv. Intell. Syst., SCIS ISIS},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Global interest in complementary and alternative medicine has increased in recent years, with Kampo medicine in Japan gaining greater trust and use. Detailed patient interviews are essential in Kampo medicine, as the physician's empathy is critical to diagnostic precision. Typically, medical students develop empathy and deepen their understanding of Kampo's pathological concepts through clinical practice. However, the COVID-19 pandemic has imposed significant restrictions on clinical training. To address this challenge, we propose a novel educational approach to enhance empathy and understanding of Kampo medicine by developing a virtual patient application. This application leverages generative artificial intelligence to simulate realistic patient interactions, enabling students to practice Kampo medical interviews in a safe, controlled environment. The AI-generated conversations are designed to reflect the emotional nuances of real-life dialogue, with the virtual patients' facial expressions synchronized to these emotions, thus enhancing the realism of the training. The suggested method allows repeated practice at any time and fosters the development of essential diag-nostic and empathetic skills. While promising challenges remain in improving these simulations' accuracy, further refinements are still under consideration. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Artificial intelligence, Clinical practices, Clinical training, Complementary and alternative medicines, Covid-19, Diagnosis, Educational approach, Empathy, Kampo medical interview, Medical education, Medical student, Medical students, New approaches, Virtual environments, Virtual patient, Virtual patient models, Virtual patients, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Global interest in complementary and alternative medicine has increased in recent years, with Kampo medicine in Japan gaining greater trust and use. Detailed patient interviews are essential in Kampo medicine, as the physician's empathy is critical to diagnostic precision. Typically, medical students develop empathy and deepen their understanding of Kampo's pathological concepts through clinical practice. However, the COVID-19 pandemic has imposed significant restrictions on clinical training. To address this challenge, we propose a novel educational approach to enhance empathy and understanding of Kampo medicine by developing a virtual patient application. This application leverages generative artificial intelligence to simulate realistic patient interactions, enabling students to practice Kampo medical interviews in a safe, controlled environment. The AI-generated conversations are designed to reflect the emotional nuances of real-life dialogue, with the virtual patients' facial expressions synchronized to these emotions, thus enhancing the realism of the training. The suggested method allows repeated practice at any time and fosters the development of essential diag-nostic and empathetic skills. While promising challenges remain in improving these simulations' accuracy, further refinements are still under consideration. © 2025 Elsevier B.V., All rights reserved.