X-Education: Education of All Things with AI and Edge Computing—One Case Study for EFL Learning

Wu-Yuin Hwang and Rio Nurtantyana ()
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Wu-Yuin Hwang: Graduate Institute of Network Learning Technology, National Central University, Zhongli District, Taoyuan City 32001, Taiwan
Rio Nurtantyana: Graduate Institute of Network Learning Technology, National Central University, Zhongli District, Taoyuan City 32001, Taiwan

Sustainability, 2022, vol. 14, issue 19, 1-23

Abstract: Education usually only focuses on how to educate human beings with pedagogical or technical support. However, with artificial intelligence (AI) and edge computing, education can be extended and considered not only to educate human beings but also all things, such as physical or digital things. In this study, all things are given the opportunity to learn more about themselves and build their knowledge through interactions with other things, people, and AI agents. Thus, the X-Education framework is proposed in this study for educating all things, including human beings, physical, digital, and AI agents. One preliminary study for EFL writing was conducted to investigate not only whether all things can speed up their knowledge but also whether EFL learners as humans can also obtain the benefits of using X-Education. Further, the forwarding mechanisms of questioning and answering (Q&A) were designed to speed up interactions among all things. In total, 22 learners were divided into two groups, the experimental group (EG) and the control group (CG), with/without the Q&A forwarding mechanisms, respectively. A mixed-method approach with the two experimental phases was used in this study. The results showed that the knowledge of all things in the EG increased significantly more than the CG. Moreover, the EG received better EFL answers from the on-device AI with the forwarding mechanisms. They also felt that X-Education could help them to learn EFL writing better through Q&A. Furthermore, it was demonstrated that X-Education can accommodate not only humans but also all things to improve their knowledge.

Keywords: edge computing; on-device AI; forwarding mechanisms; Q&A; EFL learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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