Skip to main content Skip to main navigation menu Skip to site footer
  • Register
  • Login
  • Menu
  • Home
  • Current
  • Archives
  • Announcements
  • About
    • About the Journal
    • Submissions
    • Editorial Team
    • Privacy Statement
    • Contact
  • Register
  • Login

Quality in Sport

The application and practice of artificial intelligence in promoting the development of physical education in colleges and universities: a review
  • Home
  • /
  • The application and practice of artificial intelligence in promoting the development of physical education in colleges and universities: a review
  1. Home /
  2. Archives /
  3. Vol. 48 (2025) /
  4. Physical Culture Sciences

The application and practice of artificial intelligence in promoting the development of physical education in colleges and universities: a review

Authors

  • Xiuxia Wang College of Accounting, Chongqing Finance and Economics College, Chongqing, China, 401320 https://orcid.org/0009-0005-2006-8517
  • Junjie Chen College of Artificial Intelligence, Southwest University, Chongqing, China, 400715; National & Local Joint Engineering Research Center of Intelligent Transmission and Control Technology, Chongqing, China, 400715 https://orcid.org/0009-0006-9646-3625
  • Guangxin Cheng School of Physical Education, Southwest University, Chongqing, China, 400715 https://orcid.org/0009-0003-9376-1000

DOI:

https://doi.org/10.12775/QS.2025.48.62855

Keywords

artificial intelligence, college physical education, personalized training, teaching management, integration of educational technology

Abstract

With the rapid development of artificial intelligence (AI) technology, its application in college physical education has gradually shifted from marginal exploration to system integration. Based on literature review, case study and empirical data analysis, this paper systematically explores the multi-dimensional application of AI technology in college physical education, including sports performance analysis, personalized training, teaching management optimization and intelligent evaluation. The results show that AI can effectively improve teaching efficiency, scientific training and objectivity of evaluation, while promoting the precision and fairness of physical education. Through surveys and data comparisons of multiple colleges and universities, this paper verifies the significant effectiveness of AI in improving students' sports performance, reducing sports injury rates, and improving classroom organization efficiency. At the same time, the article also points out the challenges of current applications, such as infrastructure shortage, insufficient digital literacy of teachers, low algorithm transparency and data ethics concerns. Based on this, this paper proposes a path to promote the deep integration of AI into physical education from four dimensions: system construction, teacher empowerment, technology development and data governance. The study believes that AI will play the role of "intelligent engine" in future college physical education, promoting the transformation of the education paradigm from experience-driven to data-driven.

References

[1] Long Zhong, He Qiuhong. Dilemma and solution of physical education curriculum reform under the perspective of "Healthy China"[J]. Sports World, 2025, (04): 58-60. DOI: 10.16730/j.cnki.61-1019/g8.2025.04.011.

[2] Zhang Anyang. Thoughts on artificial intelligence empowering physical education teaching reform in colleges and universities[J]. Cultural and Sports Supplies and Technology, 2025, (09): 172-174.

[3] Luo Zhong, Peng Cheng. Artificial intelligence integrated into the teaching practice of sports theory courses in colleges and universities - taking ChatGPT as an example [J]. Sports World, 2025, (03): 11-14. DOI: 10.16730/j.cnki.61-1019/g8.2025.03.001.

[4] Guo Keyu. Theoretical framework and practical exploration of innovation of college sports teaching model in the era of intelligence [J]. Contemporary Sports Science and Technology, 2025, 15 (07): 55-58. DOI: 10.16655/j.cnki.2095-2813.2025.07.01 5.

[5] Chen Ruiqi. Construction and empirical research of autonomous-cooperative-exploratory teaching model of college physical education under the perspective of deep learning [D]. Jiangxi Science and Technology Normal University, 2022. DOI: 10.27751/d.cnki.gjxkj.2022.000088.

[6] Yuan Li. Research on the application of decision tree algorithm in public physical education practical teaching in colleges and universities [J]. Journal of Xi'an Institute of Physical Education, 2011, 28(06): 765-767+783. DOI: 10.16063/j.cnki.issn1001-747x.2011.06.028.

[7] Xiao Zefang, Lin Deqiang, Lin Guichi, et al. Research on the application of intelligent testing equipment in college physical education teaching practice [J]. Contemporary Sports Science and Technology, 2025, 15(01): 187-190. DOI: 10.16655/j.cnki.2095-2813.2025.01.050.

[8] Huang J, Yu D. Application of Deep Learning in College Physical Education Design under Flipped Classroom. Comput Intell Neurosci. 2022 Sep 16;2022:7368771. doi: 10.1155/2022/7368771. PMID: 36156941; PMCID: PMC9507692.

[9] Miao Ning, Chu Xiaoyong, Gao Sheng, et al. Research on the strategy of physical education class division in colleges and universities based on deep neural network physical test analysis [C]//Chinese Society of Sports Science. Collection of abstracts of the 11th National Sports Science Conference. Zhujiang College of Tianjin University of Finance and Economics;, 2019: 5781-5783. DOI: 10.26914/c.cnkihy.2019.031531.

[10] Zhang J. College English Assisted Teaching Based on Flipped Classroom and Its Influence on Students' Learning Psychology. Occup Ther Int. 2022 Jun 18;2022:4723893. doi: 10.1155/2022/4723893. Retraction in: Occup Ther Int. 2024 Jan 24;2024:9769187. doi: 10.1155/2024/9769187. PMID: 35821711; PMCID: PMC9233606.

[11] Cui W. Research on the Effectiveness of Probabilistic Stochastic Convolution Neural Network Algorithm in Physical Education Teaching Evaluation. Comput Intell Neurosci. 2022 Apr 27;2022:4921846. doi: 10.1155/2022/4921846. PMID: 35528362; PMCID: PMC9068316.

[12] Han Zhengqiang, Chen Haiping, Xu Haofan. Application of optimized particle swarm neural network algorithm in the evaluation of physical education teaching quality in colleges and universities [C]//Chinese Society of Sports Science. Collection of abstracts of the 13th National Sports Science Conference - Special Report (Sports Statistics Branch). School of Applied Engineering, Henan University of Science and Technology; Sanmenxia Vocational and Technical College;, 2023: 49-52.DOI:10.26914/c.cnkihy.2023.061725.

[13] Zhou Guangyu. Research on the design and application of student-led teaching based on deep learning in basketball special courses in sports colleges and departments [D]. Yangtze University, 2023.DOI:10.26981/d.cnki.g jhsc.2023.001457.

[14] Zan Hui. Research on key technologies for intelligent recognition and evaluation of radio gymnastics movements[D]. Central China Normal University, 2022. DOI:10.27159/d.cnki.ghzsu.2022.004007.

[15] Ding Haifeng, Liu Yunting, Zhang Xingwei, et al. Research on pull-up detection algorithm based on improved OpenPose[J]. Communications and Information Technology, 2025, (01): 51-54.

[16] Liu Liming. Exploring new paths for artificial intelligence to empower physical training of track and field athletes[J]. Sports and Cultural Products and Technology, 2024, (24): 175-177.

[17] Li Qing, Cui Jiarui, Yang Xu, et al. Exploration of AI-enabled practical teaching management of engineering majors [J/OL]. Research on Higher Engineering Education, 1-7 [2025-05-12]. http://kns.cnki.net/kcms/detail/42.1026.G4.20250506.1524.008.html.

[18] Wang Zongqian. Accurate layered teaching of "artificial intelligence" courses under the background of new engineering [J]. Mold Manufacturing, 2024, 24(09): 114-116. DOI: 10.13596/j.cnki.44-1542/th.2024.09.038.

[19] Tian Yu, Liu Hong. Pedestrian joint point detection algorithm based on improved OpenPose [J]. Sensors and Microsystems, 2024, 43(09): 1 44-148.DOI:10.13873/J.1000-9787(2024)09-0144-05.

[20]Yang Yun, Wei Gongbo, Yu Xiang. Research on strategies and effect evaluation of sports skills and performance improvement under the perspective of artificial intelligence[J]. Sports World, 2025, (03): 28-32+36.DOI:10.16730/j.cnki.61-1019/g8.2025.03.0 05.

[21] Zhang Xiuli, Yao Siqi, Zhou Yang, et al. Application scenarios and key technical issues of artificial intelligence to promote the digital transformation of school sports [J/OL]. Sports Research, 1-15 [2025-05-12]. https://doi.org/10.15877/j.cnki.nsic.20250307.001.

[22] Wang Pu, Tan Xiangchao. Evaluation of physical education teaching quality based on artificial intelligence technology - taking the evaluation of volleyball teaching quality in primary and secondary schools as an example [J]. Sports Teaching, 2025, 45(03): 97-99.

[23] Yin Zhihua, Guo Mingming, Jia Chenyu, et al. Demand mechanism, key dimensions and implementation strategies for artificial intelligence to promote the development of physical education [J]. Journal of Chengdu Sports University, 2023, 49(02): 73-81. DOI: 10.15942/j.jcsu.2023.02.011.

Quality in Sport

Downloads

  • PDF

Published

2025-12-26

How to Cite

1.
WANG, Xiuxia, CHEN, Junjie and CHENG, Guangxin. The application and practice of artificial intelligence in promoting the development of physical education in colleges and universities: a review. Quality in Sport. Online. 26 December 2025. Vol. 48, p. 62855. [Accessed 26 December 2025]. DOI 10.12775/QS.2025.48.62855.
  • ISO 690
  • ACM
  • ACS
  • APA
  • ABNT
  • Chicago
  • Harvard
  • IEEE
  • MLA
  • Turabian
  • Vancouver
Download Citation
  • Endnote/Zotero/Mendeley (RIS)
  • BibTeX

Issue

Vol. 48 (2025)

Section

Physical Culture Sciences

License

Copyright (c) 2025 Xiuxia Wang, Junjie Chen, Guangxin Cheng

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Stats

Number of views and downloads: 3
Number of citations: 0

Search

Search

Browse

  • Browse Author Index
  • Issue archive

User

User

Current Issue

  • Atom logo
  • RSS2 logo
  • RSS1 logo

Information

  • For Readers
  • For Authors
  • For Librarians

Newsletter

Subscribe Unsubscribe

Tags

Search using one of provided tags:

artificial intelligence, college physical education, personalized training, teaching management, integration of educational technology
Up

Akademicka Platforma Czasopism

Najlepsze czasopisma naukowe i akademickie w jednym miejscu

apcz.umk.pl

Partners

  • Akademia Ignatianum w Krakowie
  • Akademickie Towarzystwo Andragogiczne
  • Fundacja Copernicus na rzecz Rozwoju Badań Naukowych
  • Instytut Historii im. Tadeusza Manteuffla Polskiej Akademii Nauk
  • Instytut Kultur Śródziemnomorskich i Orientalnych PAN
  • Instytut Tomistyczny
  • Karmelitański Instytut Duchowości w Krakowie
  • Ministerstwo Kultury i Dziedzictwa Narodowego
  • Państwowa Akademia Nauk Stosowanych w Krośnie
  • Państwowa Akademia Nauk Stosowanych we Włocławku
  • Państwowa Wyższa Szkoła Zawodowa im. Stanisława Pigonia w Krośnie
  • Polska Fundacja Przemysłu Kosmicznego
  • Polskie Towarzystwo Ekonomiczne
  • Polskie Towarzystwo Ludoznawcze
  • Towarzystwo Miłośników Torunia
  • Towarzystwo Naukowe w Toruniu
  • Uniwersytet im. Adama Mickiewicza w Poznaniu
  • Uniwersytet Komisji Edukacji Narodowej w Krakowie
  • Uniwersytet Mikołaja Kopernika
  • Uniwersytet w Białymstoku
  • Uniwersytet Warszawski
  • Wojewódzka Biblioteka Publiczna - Książnica Kopernikańska
  • Wyższe Seminarium Duchowne w Pelplinie / Wydawnictwo Diecezjalne „Bernardinum" w Pelplinie

© 2021- Nicolaus Copernicus University Accessibility statement Shop