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Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot

Author

Listed:
  • Dapeng Yang

    (College of Physical Education, HuaiNan Normal University, Huainan 232038, China
    College of Arts & Sport Sciences, Dong-A University, Busan 604-714, Korea)

  • Eung-Soo Oh

    (College of Arts & Sport Sciences, Dong-A University, Busan 604-714, Korea)

  • Yingchun Wang

    (College of Arts & Sport Sciences, Dong-A University, Busan 604-714, Korea
    Sports Section, Zhejiang Dongfang Polytechnic, Wenzhou 325015, China)

Abstract

In order to promote the development of individualized, accurate and intelligent physical education teaching, combined with artificial intelligence technology, the current physical education teaching mode has been improved. Through the establishment of an artificial intelligence educational robot based on voice interaction, a hybrid physical education teaching mode is constructed to realize personalized education for students. First, the speech recognition system is designed from three aspects of speech recognition, interaction management and speech synthesis, and the accuracy of recognition is improved by algorithm. Second, a new mode of hybrid physical education teaching is constructed. Through intelligent information technology, the advantages of traditional physical education teaching are combined to improve the classroom efficiency of physical education teaching and personalized education ability for students. Finally, the relevant experimental scheme and questionnaire are designed, and the actual situation of an educational robot introduced into physical education teaching is investigated and evaluated. The results show that the recognition accuracy of the artificial intelligence speech recognition system can reach more than 90%. It can communicate well with students and answer students’ questions. An educational robot is introduced into physical education teaching, and students’ learning attitude and interest are evaluated. The results show that before and after the introduction of an educational robot in physical education teaching, the average score of students’ learning interest increases by 21 points, and the average score of learning attitude increases by 9.8 points. Therefore, the introduction of an artificial intelligence educational robot based on voice interaction in physical education teaching can help to improve the classroom efficiency of physical education teaching and students’ interest. This study provides a reference for the development of artificial intelligence teaching and promoting the development of artificial intelligence.

Suggested Citation

  • Dapeng Yang & Eung-Soo Oh & Yingchun Wang, 2020. "Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot," Sustainability, MDPI, vol. 12(19), pages 1-14, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:8000-:d:420529
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    References listed on IDEAS

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    1. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    2. Yi-Hsiang Pan & Chen-Hui Huang & I-Sheng Lee & Wei-Ting Hsu, 2019. "Comparison of Learning Effects of Merging TPSR Respectively with Sport Education and Traditional Teaching Model in High School Physical Education Classes," Sustainability, MDPI, vol. 11(7), pages 1-15, April.
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    Cited by:

    1. Pei-Yao Su & Zi-Ying Zhao & Qi-Gan Shao & Pei-Yuan Lin & Zhe Li, 2023. "The Construction of an Evaluation Index System for Assistive Teaching Robots Aimed at Sustainable Learning," Sustainability, MDPI, vol. 15(17), pages 1-24, September.
    2. Yanwei You & Yuquan Chen & Yujun You & Qi Zhang & Qiang Cao, 2023. "Evolutionary Game Analysis of Artificial Intelligence Such as the Generative Pre-Trained Transformer in Future Education," Sustainability, MDPI, vol. 15(12), pages 1-12, June.

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