@inproceedings{696234ba56d84ae3a8fc5254d69cf55d,
title = "A Pet-Like Model for Educational Robots: Using Interdependence Theory to Enhance Learning and Sustain Long-Term Relationships",
abstract = "Educational robotics research endeavors to develop companion robots that can engage students in learning activities. However, existing educational robots are not always well-suited for use outside of the classroom and may struggle to sustain compelling interactions. To address this, this study proposes a new model for educational robots that leverages interdependence theory, which explains how mutual dependence can lead to lasting relationships. The pet-owner dynamic, in which the pet relies on the care and attention of the owner and provides emotional support in return, served as inspiration for this model. The proposed model features a digital pet-like robot that students must train and empower to perform a drama presenting their final learning results in front of the class. In an experiment with 60 students in a Japanese Hospitality course in Taiwan, the study found that long-term relationships between students and pet-like robots emerged, and these relationships significantly improved learning performance compared to ordinary robots. This study highlights the potential of integrating interdependence theory with advanced technologies such as robots, mobile devices, and virtual reality to enhance student learning and foster long-term relationships between students and robots.",
keywords = "educational robot, human-robot interaction, interdependence theory, long-term relationship, pet, robotic pet",
author = "{Al Hakim}, {Vando Gusti} and Yang, {Su Hang} and Wang, {Jen Hang} and Chang, {Yu Chen} and Lin, {Hung Hsuan} and Chen, {Gwo Dong}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Advanced Learning Technologies, ICALT 2023 ; Conference date: 10-07-2023 Through 13-07-2023",
year = "2023",
doi = "10.1109/ICALT58122.2023.00035",
language = "???core.languages.en_GB???",
series = "Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "100--104",
editor = "Maiga Chang and Nian-Shing Chen and Rita Kuo and George Rudolph and Sampson, {Demetrios G} and Ahmed Tlili",
booktitle = "Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023",
}