A Pet-Like Model for Educational Robots: Using Interdependence Theory to Enhance Learning and Sustain Long-Term Relationships

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Vando Gusti Al Hakim, Su-Hang Yang, Jen-Hang Wang, Yu-Chen Chang, Hung-Hsuan Lin, Gwo-Dong Chen

2023 Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023 Conference paper Cited by 6 Quartile

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. © 2023 IEEE.

Affiliations

National Central University, Department of Computer Science and Information Engineering, Taoyuan, Taiwan; Yogyakarta State University, Department of Electrical Engineering Education, Yogyakarta, Indonesia; Chien Hsin University of Science and Technology, Department of Hospitality Management, Taoyuan, Taiwan; Research Center for Science and Technology for Learning, National Central University, Taoyuan, Taiwan

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