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 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.
2024
Domenichini, D.; Bucchiarone, A.; Chiarello, F.; Schiavo, G.; Fantoni, G.
An AI-Driven Approach for Enhancing Engagement and Conceptual Understanding in Physics Education Proceedings Article
In: IEEE Global Eng. Edu. Conf., EDUCON, IEEE Computer Society, 2024, ISBN: 21659559 (ISSN); 979-835039402-3 (ISBN).
Abstract | Links | BibTeX | Tags: Adaptive Learning, Artificial intelligence, Artificial intelligence in education, Artificial Intelligence in Education (AIED), Conceptual Understanding, Educational System, Educational systems, Gamification, Generative AI, generative artificial intelligence, Learning Activity, Learning systems, Physics Education, Teachers', Teaching, Virtual Reality
@inproceedings{domenichini_ai-driven_2024,
title = {An AI-Driven Approach for Enhancing Engagement and Conceptual Understanding in Physics Education},
author = {D. Domenichini and A. Bucchiarone and F. Chiarello and G. Schiavo and G. Fantoni},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199035695&doi=10.1109%2fEDUCON60312.2024.10578670&partnerID=40&md5=4cf9f89e97664ae6d618a90f2dbc23e0},
doi = {10.1109/EDUCON60312.2024.10578670},
isbn = {21659559 (ISSN); 979-835039402-3 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {IEEE Global Eng. Edu. Conf., EDUCON},
publisher = {IEEE Computer Society},
abstract = {This Work in Progress paper introduces the design of an innovative educational system that leverages Artificial Intelligence (AI) to address challenges in physics education. The primary objective is to create a system that dynamically adapts to the individual needs and preferences of students while maintaining user-friendliness for teachers, allowing them to tailor their teaching methods. The emphasis is on fostering motivation and engagement, achieved through the implementation of a gamified virtual environment and a strong focus on personalization. Our aim is to develop a system capable of autonomously generating learning activities and constructing effective learning paths, all under the supervision and interaction of teachers. The generation of learning activities is guided by educational taxonomies that delineate and categorize the cognitive processes involved in these activities. The proposed educational system seeks to address challenges identified by Physics Education Research (PER), which offers valuable insights into how individuals learn physics and provides strategies to enhance the overall quality of physics education. Our specific focus revolves around two crucial aspects: concentrating on the conceptual understanding of physics concepts and processes, and fostering knowledge integration and coherence across various physics topics. These aspects are deemed essential for cultivating enduring knowledge and facilitating practical applications in the field of physics. © 2024 IEEE.},
keywords = {Adaptive Learning, Artificial intelligence, Artificial intelligence in education, Artificial Intelligence in Education (AIED), Conceptual Understanding, Educational System, Educational systems, Gamification, Generative AI, generative artificial intelligence, Learning Activity, Learning systems, Physics Education, Teachers', Teaching, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This Work in Progress paper introduces the design of an innovative educational system that leverages Artificial Intelligence (AI) to address challenges in physics education. The primary objective is to create a system that dynamically adapts to the individual needs and preferences of students while maintaining user-friendliness for teachers, allowing them to tailor their teaching methods. The emphasis is on fostering motivation and engagement, achieved through the implementation of a gamified virtual environment and a strong focus on personalization. Our aim is to develop a system capable of autonomously generating learning activities and constructing effective learning paths, all under the supervision and interaction of teachers. The generation of learning activities is guided by educational taxonomies that delineate and categorize the cognitive processes involved in these activities. The proposed educational system seeks to address challenges identified by Physics Education Research (PER), which offers valuable insights into how individuals learn physics and provides strategies to enhance the overall quality of physics education. Our specific focus revolves around two crucial aspects: concentrating on the conceptual understanding of physics concepts and processes, and fostering knowledge integration and coherence across various physics topics. These aspects are deemed essential for cultivating enduring knowledge and facilitating practical applications in the field of physics. © 2024 IEEE.
Arrigo, M.; Farella, M.; Fulantelli, G.; Schicchi, D.; Taibi, D.
A Task-Interaction Framework to Monitor Mobile Learning Activities Based on Artificial Intelligence and Augmented Reality Proceedings Article
In: L.T., De Paolis; P., Arpaia; M., Sacco (Ed.): Lect. Notes Comput. Sci., pp. 325–333, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303171706-2 (ISBN).
Abstract | Links | BibTeX | Tags: Activity-based, Adversarial machine learning, Analytic technique, Augmented Reality, Contrastive Learning, Federated learning, Generative AI, Interaction framework, Learning Activity, Learning analytic framework, Learning Analytics Framework, Learning experiences, Learning patterns, Mobile Learning, Teachers'
@inproceedings{arrigo_task-interaction_2024,
title = {A Task-Interaction Framework to Monitor Mobile Learning Activities Based on Artificial Intelligence and Augmented Reality},
author = {M. Arrigo and M. Farella and G. Fulantelli and D. Schicchi and D. Taibi},
editor = {De Paolis L.T. and Arpaia P. and Sacco M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204618733&doi=10.1007%2f978-3-031-71707-9_26&partnerID=40&md5=8969f18ab0f10dcddf37e54265d10518},
doi = {10.1007/978-3-031-71707-9_26},
isbn = {03029743 (ISSN); 978-303171706-2 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15027 LNCS},
pages = {325–333},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The complexity behind the analysis of mobile learning activities has requested the development of specifically designed frameworks. When students are involved in mobile learning experiences, they interact with the context in which the activities occur, the content they have access to, with peers and their teachers. The wider adoption of generative artificial intelligence introduces new interactions that researchers have to look at when learning analytics techniques are applied to monitor learning patterns. The task interaction framework proposed in this paper explores how AI-based tools affect student-content and student-context interactions during mobile learning activities, thus focusing on the interplay of Learning Analytics and Artificial Intelligence advances in the educational domain. A use case scenario that explores the framework’s application in a real educational context is also presented. Finally, we describe the architectural design of an environment that leverages the task interaction framework to analyze enhanced mobile learning experiences in which structured content extracted from a Knowledge Graph is elaborated by a large language model to provide students with personalized content. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Activity-based, Adversarial machine learning, Analytic technique, Augmented Reality, Contrastive Learning, Federated learning, Generative AI, Interaction framework, Learning Activity, Learning analytic framework, Learning Analytics Framework, Learning experiences, Learning patterns, Mobile Learning, Teachers'},
pubstate = {published},
tppubtype = {inproceedings}
}
The complexity behind the analysis of mobile learning activities has requested the development of specifically designed frameworks. When students are involved in mobile learning experiences, they interact with the context in which the activities occur, the content they have access to, with peers and their teachers. The wider adoption of generative artificial intelligence introduces new interactions that researchers have to look at when learning analytics techniques are applied to monitor learning patterns. The task interaction framework proposed in this paper explores how AI-based tools affect student-content and student-context interactions during mobile learning activities, thus focusing on the interplay of Learning Analytics and Artificial Intelligence advances in the educational domain. A use case scenario that explores the framework’s application in a real educational context is also presented. Finally, we describe the architectural design of an environment that leverages the task interaction framework to analyze enhanced mobile learning experiences in which structured content extracted from a Knowledge Graph is elaborated by a large language model to provide students with personalized content. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Jia, Y.; Sin, Z. P. T.; Wang, X. E.; Li, C.; Ng, P. H. F.; Huang, X.; Dong, J.; Wang, Y.; Baciu, G.; Cao, J.; Li, Q.
NivTA: Towards a Naturally Interactable Edu-Metaverse Teaching Assistant for CAVE Proceedings Article
In: Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom, pp. 57–64, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833151599-7 (ISBN).
Abstract | Links | BibTeX | Tags: Active learning, Adversarial machine learning, cave automatic virtual environment, Cave automatic virtual environments, Caves, Chatbots, Contrastive Learning, Digital elevation model, Federated learning, Interactive education, Language Model, Large language model agent, Learning Activity, LLM agents, Metaverses, Model agents, Natural user interface, Students, Teaching, Teaching assistants, Virtual environments, Virtual Reality, virtual teaching assistant, Virtual teaching assistants
@inproceedings{jia_nivta_2024,
title = {NivTA: Towards a Naturally Interactable Edu-Metaverse Teaching Assistant for CAVE},
author = {Y. Jia and Z. P. T. Sin and X. E. Wang and C. Li and P. H. F. Ng and X. Huang and J. Dong and Y. Wang and G. Baciu and J. Cao and Q. Li},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211447638&doi=10.1109%2fMetaCom62920.2024.00023&partnerID=40&md5=efefd453c426e74705518254bdc49e87},
doi = {10.1109/MetaCom62920.2024.00023},
isbn = {979-833151599-7 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom},
pages = {57–64},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Edu-metaverse is a specialized metaverse dedicated for interactive education in an immersive environment. Its main purpose is to immerse the learners in a digital environment and conduct learning activities that could mirror reality. Not only does it enable activities that may be difficult to perform in the real world, but it also extends the interaction to personalized and CL. This is a more effective pedagogical approach as it tends to enhance the motivation and engagement of students and it increases their active participation in lessons delivered. To this extend, we propose to realize an interactive virtual teaching assistant called NivTA. To make NivTA easily accessible and engaging by multiple users simultaneously, we also propose to use a CAVE virtual environment (CAVE-VR) as a "metaverse window"into concepts, ideas, topics, and learning activities. The students simply need to step into the CAVE-VR and interact with a life-size teaching assistant that they can engage with naturally, as if they are approaching a real person. Instead of text-based interaction currently developed for large language models (LLM), NivTA is given additional cues regarding the users so it can react more naturally via a specific prompt design. For example, the user can simply point to an educational concept and ask NivTA to explain what it is. To guide NivTA onto the educational concept, the prompt is also designed to feed in an educational KG to provide NivTA with the context of the student's question. The NivTA system is an integration of several components that are discussed in this paper. We further describe how the system is designed and implemented, along with potential applications and future work on interactive collaborative edu-metaverse environments dedicated for teaching and learning. © 2024 IEEE.},
keywords = {Active learning, Adversarial machine learning, cave automatic virtual environment, Cave automatic virtual environments, Caves, Chatbots, Contrastive Learning, Digital elevation model, Federated learning, Interactive education, Language Model, Large language model agent, Learning Activity, LLM agents, Metaverses, Model agents, Natural user interface, Students, Teaching, Teaching assistants, Virtual environments, Virtual Reality, virtual teaching assistant, Virtual teaching assistants},
pubstate = {published},
tppubtype = {inproceedings}
}
Edu-metaverse is a specialized metaverse dedicated for interactive education in an immersive environment. Its main purpose is to immerse the learners in a digital environment and conduct learning activities that could mirror reality. Not only does it enable activities that may be difficult to perform in the real world, but it also extends the interaction to personalized and CL. This is a more effective pedagogical approach as it tends to enhance the motivation and engagement of students and it increases their active participation in lessons delivered. To this extend, we propose to realize an interactive virtual teaching assistant called NivTA. To make NivTA easily accessible and engaging by multiple users simultaneously, we also propose to use a CAVE virtual environment (CAVE-VR) as a "metaverse window"into concepts, ideas, topics, and learning activities. The students simply need to step into the CAVE-VR and interact with a life-size teaching assistant that they can engage with naturally, as if they are approaching a real person. Instead of text-based interaction currently developed for large language models (LLM), NivTA is given additional cues regarding the users so it can react more naturally via a specific prompt design. For example, the user can simply point to an educational concept and ask NivTA to explain what it is. To guide NivTA onto the educational concept, the prompt is also designed to feed in an educational KG to provide NivTA with the context of the student's question. The NivTA system is an integration of several components that are discussed in this paper. We further describe how the system is designed and implemented, along with potential applications and future work on interactive collaborative edu-metaverse environments dedicated for teaching and learning. © 2024 IEEE.