Skip to main content

Research on the Influence of Artificial Intelligence Interactive Function on Youth Sports Training – Taking Tiantian Skipping Rope App as an Example

  • Conference paper
  • First Online:
HCI International 2023 – Late Breaking Papers (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14060))

Included in the following conference series:

Abstract

This study aims to explore the teaching effect of sports training app integrated with artificial intelligence interactive function as a teaching aid for teenagers. In this study, we compared the learning interest, learning attitude, learning behavior and willingness to continue learning of 123 children aged 7–12 years in the course of physical training. The subjects were divided into two groups: the experimental group used artificial intelligence interactive app to assist physical education, and the control group used traditional teaching methods for physical training and learning. The results showed that there is no significant difference in the influence of gender on AI interactive sports learning interest, learning attitude, learning behavior and willingness to continue learning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. https://mp.weixin.qq.com/s?__biz=MjM5MTMxNzUyMg==&mid=2651061802&idx=1&sn=6f8e98ef9a1d9321277168dd09b0dde5&chksm=bd4035578a37bc41d2466fc8705b43e405d3254ebd679766c3d09f38b7a1f32d8bc0a1bfb8b0&scene=27

  2. Yonghe, W., Bowen, L., Xiaoling, M.: Build an “artificial intelligence + education” eco-system. J. Dist. Educ. 35(05), 27–39 (2017)

    Google Scholar 

  3. Yuanbo, L.: Design and practice of artificial intelligence in physical education teaching system. Mechanical design 38(6), 165166 (2021)

    Google Scholar 

  4. Hongcheng, C., Qingguo, C.: Research on continuous use intention of mobile fit-ness app users. J. Capital Univ. Phys. Educ. 32(01), 75–81+96 (2020)

    Google Scholar 

  5. Ruoxi, W., Qingjun, W.: The development status, problems and countermeasures of sports fitness APP. J. Shandong Univ. Phys. Educ. 004, 18–22 (2015)

    Google Scholar 

  6. Jossa-Bastidas, O., et al.: Predicting physical exercise adherence in fitness apps using a deep learning approach. Int. J. Environ. Res. Public Health 18(20), 10769 (2021)

    Google Scholar 

  7. Yeqiao, W.: Research on brand communication strategies and effects of fitness apps. Guangxi University (2017)

    Google Scholar 

  8. Muntaner-Mas, A., et al.: Smartphone app (2kmFIT-App) for measuring cardi-orespiratory fitness: validity and reliability study. JMIR mHealth and uHealth 9.1 e14864,(2021)

    Google Scholar 

  9. Jiang, L.C., Sun, M., Huang, G.:Uncovering the heterogeneity in fitness app use: a latent class analysis of Chinese users. Int. J. Environ. Res. Public Health 19(17) 10679 (2022)

    Google Scholar 

  10. Rospo, G., et al.: Cardiorespiratory improvements achieved by American college of sports medicine’s exercise prescription implemented on a mobile app. JMIR mHealth and uHealth 4(2), e5518 (2016)

    Google Scholar 

  11. Weibo, F.: Analysis and Integration of Urban Recreation Space. Chong-qing University, Chongqing (2007)

    Google Scholar 

  12. Dingwei, Z., Liesheng, C.: Design and research of intelligent interaction in fresh fruit and vegetable packaging. Packaging Eng. 1–8 (2021)

    Google Scholar 

  13. Jing, C., Tingguan, H., Di, C.: Yu interactive landscape experience enhancement design. (Small. Landscape Archit. 6(02), 30–41 (2018)

    Google Scholar 

  14. Wenfeng, W., Rong, Z.: Application of intelligent technology in interactive painting design and its creation mechanism research. Packag. Eng.. Eng. 43(S1), 89–95 (2022)

    Google Scholar 

  15. Wu, Z., Ji, D., Yu, K., et al.: AI Creativity and the Human-AI Co-Creation Model. Remin Publish, Beijing (2021)

    Book  Google Scholar 

  16. Renninger, K.A., Hidi, S.E.: Interest development, self-related information processing, and practice. Theor. Pract. 61(1), 23–34 (2022)

    Google Scholar 

  17. Mehrabian, A.: Individual differences in achieving tendency: review of evidence bearing on a questionnaire measure. Curr. Psychol.. Psychol. 13, 351–364 (1994)

    Article  Google Scholar 

  18. Yu, Z.: The effects of the superstar learning system on learning interest, attitudes, and academic achievements. Multimedia Tools Appl. 82, 17947–17962 (2022)

    Google Scholar 

  19. Zhou, W., et al.: Deep learning modeling for top-n recommendation with interests exploring. IEEE Access 6, 51440–51455 (2018)

    Google Scholar 

  20. Bhattacherjee, A.: Understanding information systems continuance: an expectationcon-firmation model. MIS Q. 25, 351–370 (2001)

    Google Scholar 

  21. Oliver, R.L.: A cognitive model of the antecedents and consequences of satisfaction decisions. J. Mark. Res. 17(4), 460–469 (1980)

    Article  Google Scholar 

  22. Wu, C.-H., Liu, C.-H., Huang, Y.-M.: The exploration of continuous learning intention in STEAM education through attitude, motivation, and cognitive load. Int. J. STEM Educ. 9(1), 1–22 (2022)

    Article  Google Scholar 

  23. Chai, C.S., et al.: Modeling Chinese secondary school students’ behavioral intentions to learn artificial intelligence with the theory of planned behavior and self-determination theory. Sustainability 15(1), 605 (2022)

    Google Scholar 

  24. de Jong, P.G.M., et al.: Development and application of a massive open online course to deliver innovative transplant education. Transplant Immunol. 66, 101339 (2021)

    Google Scholar 

  25. Fishhein, A.: Taking and information handling in consuner behavior. Boston-Graduate School of Business Administration, Harward University, pp. 176–210 (1975)

    Google Scholar 

  26. https://baike.baidu.com/item/天天跳绳/58496102?fr=aladdin

    Google Scholar 

  27. Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yin Cui .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, L., Cui, Y., Gong, X., Liu, F. (2023). Research on the Influence of Artificial Intelligence Interactive Function on Youth Sports Training – Taking Tiantian Skipping Rope App as an Example. In: Zaphiris, P., et al. HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14060. Springer, Cham. https://doi.org/10.1007/978-3-031-48060-7_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48060-7_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48059-1

  • Online ISBN: 978-3-031-48060-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics