AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our publications.
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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.
2024
Ivanova, M.; Grosseck, G.; Holotescu, C.
Unveiling Insights: A Bibliometric Analysis of Artificial Intelligence in Teaching Journal Article
In: Informatics, vol. 11, no. 1, 2024, ISSN: 22279709 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, ChatGPT, Intelligent Environment, large language models, learning analytics, Teaching
@article{ivanova_unveiling_2024,
title = {Unveiling Insights: A Bibliometric Analysis of Artificial Intelligence in Teaching},
author = {M. Ivanova and G. Grosseck and C. Holotescu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188949348&doi=10.3390%2finformatics11010010&partnerID=40&md5=aaf44928fb594e2807234da0f3799437},
doi = {10.3390/informatics11010010},
issn = {22279709 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Informatics},
volume = {11},
number = {1},
abstract = {The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the “big picture” considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query “artificial intelligence and teaching” over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field. © 2024 by the authors.},
keywords = {Artificial intelligence, ChatGPT, Intelligent Environment, large language models, learning analytics, Teaching},
pubstate = {published},
tppubtype = {article}
}
The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the “big picture” considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query “artificial intelligence and teaching” over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field. © 2024 by the authors.
2014
Augello, Agnese; Gaglio, Salvatore
Detection of User Activities in Intelligent Environments Journal Article
In: Advances in Intelligent Systems and Computing, vol. 260, pp. 19–32, 2014, ISSN: 21945357.
Abstract | Links | BibTeX | Tags: Ambient intelligence, Behavioral Research, Intelligent Environment, User Behavior Analysis
@article{augelloDetectionUserActivities2014,
title = {Detection of User Activities in Intelligent Environments},
author = { Agnese Augello and Salvatore Gaglio},
doi = {10.1007/978-3-319-03992-3_2},
issn = {21945357},
year = {2014},
date = {2014-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {260},
pages = {19--32},
abstract = {Research on Ambient Intelligence (AmI) focuses on the development of smart environments adaptable to the needs and preferences of their inhabitants. For this reason it is important to understand and model user preferences. In this chapter we describe a system to detect user behavior patterns in an intelligent workplace. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science at the University of Palermo (Italy). textcopyright Springer International Publishing Switzerland 2014.},
keywords = {Ambient intelligence, Behavioral Research, Intelligent Environment, User Behavior Analysis},
pubstate = {published},
tppubtype = {article}
}
Research on Ambient Intelligence (AmI) focuses on the development of smart environments adaptable to the needs and preferences of their inhabitants. For this reason it is important to understand and model user preferences. In this chapter we describe a system to detect user behavior patterns in an intelligent workplace. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science at the University of Palermo (Italy). textcopyright Springer International Publishing Switzerland 2014.
Augello, Agnese; Gaglio, Salvatore
Detection of user activities in intelligent environments Journal Article
In: Advances in Intelligent Systems and Computing, vol. 260, pp. 19–32, 2014, ISSN: 21945357.
Abstract | Links | BibTeX | Tags: Ambient intelligence, Behavioral Research, Intelligent Environment, User Behavior Analysis
@article{augello_detection_2014,
title = {Detection of user activities in intelligent environments},
author = {Agnese Augello and Salvatore Gaglio},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903729976&doi=10.1007%2f978-3-319-03992-3_2&partnerID=40&md5=5280f33f184d7723e4506e1cb87438aa},
doi = {10.1007/978-3-319-03992-3_2},
issn = {21945357},
year = {2014},
date = {2014-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {260},
pages = {19–32},
abstract = {Research on Ambient Intelligence (AmI) focuses on the development of smart environments adaptable to the needs and preferences of their inhabitants. For this reason it is important to understand and model user preferences. In this chapter we describe a system to detect user behavior patterns in an intelligent workplace. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science at the University of Palermo (Italy). © Springer International Publishing Switzerland 2014.},
keywords = {Ambient intelligence, Behavioral Research, Intelligent Environment, User Behavior Analysis},
pubstate = {published},
tppubtype = {article}
}
Research on Ambient Intelligence (AmI) focuses on the development of smart environments adaptable to the needs and preferences of their inhabitants. For this reason it is important to understand and model user preferences. In this chapter we describe a system to detect user behavior patterns in an intelligent workplace. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science at the University of Palermo (Italy). © Springer International Publishing Switzerland 2014.
2011
Augello, Agnese; Ortolani, Marco; Re, Giuseppe Lo; Gaglio, Salvatore
Sensor Mining for User Behavior Profiling in Intelligent Environments Journal Article
In: Studies in Computational Intelligence, vol. 361, pp. 143–158, 2011, ISSN: 1860949X.
Abstract | Links | BibTeX | Tags: Intelligent Environment, User Profiling
@article{augelloSensorMiningUser2011,
title = {Sensor Mining for User Behavior Profiling in Intelligent Environments},
author = { Agnese Augello and Marco Ortolani and Giuseppe Lo Re and Salvatore Gaglio},
editor = { Soro A. Vargiu E. Pallotta V.},
doi = {10.1007/978-3-642-21384-7_10},
issn = {1860949X},
year = {2011},
date = {2011-01-01},
journal = {Studies in Computational Intelligence},
volume = {361},
pages = {143--158},
abstract = {The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users' habit profiles are implicitly described by sensory data, which explicitly show the consequences of users' actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University. textcopyright 2011 Springer-Verlag Berlin Heidelberg.},
keywords = {Intelligent Environment, User Profiling},
pubstate = {published},
tppubtype = {article}
}
The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users' habit profiles are implicitly described by sensory data, which explicitly show the consequences of users' actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University. textcopyright 2011 Springer-Verlag Berlin Heidelberg.
Augello, Agnese; Ortolani, Marco; Re, Giuseppe Lo; Gaglio, Salvatore
Sensor mining for user behavior profiling in intelligent environments Journal Article
In: Studies in Computational Intelligence, vol. 361, pp. 143–158, 2011, ISSN: 1860949X.
Abstract | Links | BibTeX | Tags: Intelligent Environment, User Profiling
@article{augello_sensor_2011,
title = {Sensor mining for user behavior profiling in intelligent environments},
author = {Agnese Augello and Marco Ortolani and Giuseppe Lo Re and Salvatore Gaglio},
editor = {Vargiu E. Soro A. Pallotta V.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961072111&doi=10.1007%2f978-3-642-21384-7_10&partnerID=40&md5=cbc2662d7b48999129237e4163aa07b1},
doi = {10.1007/978-3-642-21384-7_10},
issn = {1860949X},
year = {2011},
date = {2011-01-01},
journal = {Studies in Computational Intelligence},
volume = {361},
pages = {143–158},
abstract = {The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users' habit profiles are implicitly described by sensory data, which explicitly show the consequences of users' actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University. © 2011 Springer-Verlag Berlin Heidelberg.},
keywords = {Intelligent Environment, User Profiling},
pubstate = {published},
tppubtype = {article}
}
The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users' habit profiles are implicitly described by sensory data, which explicitly show the consequences of users' actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University. © 2011 Springer-Verlag Berlin Heidelberg.