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sis 23(4): e12

Research Article

Design of Spoken English Teaching Based on Artificial Intelligence Educational Robots and Wireless Network Technology

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  • @ARTICLE{10.4108/eetsis.v10i3.3048,
        author={Binbin Liu  and Zhen Lu },
        title={Design of Spoken English Teaching Based on Artificial Intelligence Educational Robots and Wireless Network Technology},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={10},
        number={4},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={5},
        keywords={Artificial intelligence, computer vision, capsnet, wireless technology, gesture recognition},
        doi={10.4108/eetsis.v10i3.3048}
    }
    
  • Binbin Liu
    Zhen Lu
    Year: 2023
    Design of Spoken English Teaching Based on Artificial Intelligence Educational Robots and Wireless Network Technology
    SIS
    EAI
    DOI: 10.4108/eetsis.v10i3.3048
Binbin Liu 1,*, Zhen Lu 2
  • 1: Changsha Medical University
  • 2: Hunan Branch, China Mobile Group Design Institute Co., Ltd., Hunan, China
*Contact email: binbinliu12135@gmail.com

Abstract

Introduction: The use of AI in education can give students a more engaging learning environment and boost their motivation, and it also represents a continuation of research into the problem of human individuality in the modern era. Objectives: This paper examines the challenge of human individuality in Artificial intelligence with the Capsule network (CapsNet) scheme from two vantage points: the practical need to address issues that have arisen with the latest wave of AI advancements and a philosophical examination of how AI has already been put to use in a variety of industries. Methodology: This article investigates the new Internet spoken English teaching method, describing its benefits and providing solutions to its drawbacks, and it describes in detail how wireless technology will be implemented into online spoken English teaching. The technology provides visual representations of each stage of the gesture recognition process to aid learning. The interactive interface guides students through the gesture recognition system using computer vision applications, allowing them to encounter it firsthand; then, the sophisticated and abstract action recognition method is described with a representational illustration, which is helpful for students in elementary and secondary school to gain a more thorough understanding of and develop their capacity for logical reasoning. This will benefit students at elementary and secondary levels because it will help them think more critically and thoroughly. As a final step, we devise an experiment to compare the results of using our CapsNet method to acquire AI knowledge with those of more conventional learning strategies. Results: Experimental findings were analyzed to demonstrate that this approach is useful for acquiring CapsNet and AI and that it increases users' motivation to study and their practical competence.

Keywords
Artificial intelligence, computer vision, capsnet, wireless technology, gesture recognition
Received
2023-02-16
Accepted
2023-03-12
Published
2023-05-05
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.v10i3.3048

Copyright © 2023 Binbin Liu et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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