Research on AI Interaction to Promote Junior School Students’ Interest in ‎Physical Learning and Health Behaviors: A Big Data Analysis

  • Authors

    • Xupeng Zhang School of Education, College of Arts & Sciences, Universiti Utara Malaysia, Malaysia
    • Mohd Kasri Saidon School of Education, College of Arts & Sciences, Universiti Utara Malaysia, Malaysia
    • 'Aisyah Binti Kamaruddin School of Education, College of Arts & Sciences, Universiti Utara Malaysia, Malaysia
    https://doi.org/10.14419/jkh57464

    Received date: July 31, 2025

    Accepted date: August 29, 2025

    Published date: September 7, 2025

  • AI-Driven Tools; Physical Education; Health Behaviors; Student Engagement; Big Data Analysis

  • Abstract

    This research investigates the potential impact of AI-driven tools on student engagement in physical education, focusing on physical activity ‎participation, health behaviors, and overall well-being. A quantitative approach with big data analysis was employed to analyze how AI ‎interactions influenced junior school students' interest in physical education and their health outcomes. Data were collected from AI-powered educational platforms tracking students' physical activities, health behaviors, and engagement with physical education programs. A ‎total of 1,000 students from Shandong Province participated in the study, and the data were processed using regression analysis and ma-‎machine learning models. The results showed a significant positive correlation between AI interaction frequency and both physical activity ‎participation and health behaviors. The findings indicate that as the frequency of AI interactions increased, students' physical activity levels ‎and health behaviors improved, suggesting that AI-driven tools are effective in promoting healthier lifestyles. The research also highlights ‎that personalized AI interventions, such as virtual coaches and gamified fitness apps, played a critical role in increasing student engagement ‎in physical education. However, the limitation of this study is that the sample is confined to junior high school students in Shandong Prov-‎ince, and the results may not fully reflect the situation in other regions or age groups. Future research could expand the sample size and ‎examine the long-term effects of AI tools in various educational settings to provide further insights into their potential for improving student ‎health outcomes and education quality.

  • References

    1. Afzaal, M., Nouri, J., Zia, A., Papapetrou, P., Fors, U., Wu, Y., Li, X., & Weegar, R. (2021). Explainable AI for data-driven feedback and intelligent action recommendations to support students' self-regulation. Frontiers in Artificial Intelligence, 4. https://doi.org/10.3389/frai.2021.723447.
    2. Ahmed, A., Aziz, S., Qidwai, U., Farooq, F., Shan, J., Subramanian, M., Chouchane, L., EINatour, R., Abd-Alrazaq, A., Pandas, S., & Sheikh, J. (2023). Wearable Artificial Intelligence for Assessing Physical Activity in High School Children. Sustainability, 15(1), 638. https://doi.org/10.3390/su15010638.
    3. Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, baaa010. https://doi.org/10.1093/database/baaa010.
    4. Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a Human-In-The-Loop model. Computers and Education: Artificial Intelligence, 3, 100068. https://doi.org/10.1016/j.caeai.2022.100068.
    5. Chen, A., & Ennis, C. D. (2004). Goals, Interests, and Learning in Physical Education. The Journal of Educational Research, 97(6), 329–339. https://doi.org/10.3200/JOER.97.6.329-339.
    6. Christodoulos, A. D., Douda, H. T., Polykratis, M., & et al. (2006). Attitudes towards exercise and physical activity behaviours in Greek schoolchildren after a year-long health education intervention. British Journal of Sports Medicine, 40, 367–371. https://doi.org/10.1136/bjsm.2005.024521.
    7. Cui, B., Jiao, W., Gui, S., Li, Y., & Fang, Q. (2025). Innovating physical education with artificial intelligence: A potential approach. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1490966.
    8. Deng, C., Feng, L., & Ye, Q. (2024). Smart physical education: Governance of school physical education in the era of new generation of information technology and knowledge. Journal of Knowledge Economics, 15, 13857–13889. https://doi.org/10.1007/s13132-023-01668-0.
    9. Emerson, R. W. (2022). ANOVA Assumptions. Journal of Visual Impairment & Blindness, 116(4), 585-586. https://doi.org/10.1177/0145482X221124187.
    10. Fernandez-Rio, J., & Iglesias, D. (2022). What do we know about pedagogical models in physical education so far? An umbrella review. Physical Education and Sport Pedagogy, 29(2), 190–205. https://doi.org/10.1080/17408989.2022.2039615.
    11. Gardner, B., Arden, M. A., Brown, D., Eves, F. F., Green, J., Hamilton, K., … Lally, P. (2021). Developing habit-based health behaviour change interventions: twenty-one questions to guide future research. Psychology & Health, 38(4), 518–540. https://doi.org/10.1080/08870446.2021.2003362.
    12. George, A. S. (2023). Preparing students for an AI-driven world: Rethinking curriculum and pedagogy in the age of artificial intelligence. Partners Universal Innovative Research Publication, 1(2), 112–136.
    13. González Grandón, T., Schwenzer, J., Steens, T., & Breuing, J. (2024). Electricity demand forecasting with hybrid classical statistical and machine learning algorithms: Case study of Ukraine. Applied Energy, 355, 122249. https://doi.org/10.1016/j.apenergy.2023.122249.
    14. Goodyear, V. A., Skinner, B., McKeever, J., & Griffiths, M. (2021). The influence of online physical activity interventions on children and young people’s engagement with physical activity: a systematic review. Physical Education and Sport Pedagogy, 28(1), 94–108. https://doi.org/10.1080/17408989.2021.1953459.
    15. Guo, Q., Li, B., & Saravanan, V. (2020). Role of AI physical education based on application of functional sports training. Journal of Intelligent & Fuzzy Systems, 40(2), 3337–3345. https://doi.org/10.3233/JIFS-189373.
    16. Hu, Z., Liu, Z., & Su, Y. (2024). AI-Driven Smart Transformation in Physical Education: Current Trends and Future Research Directions. Applied Sciences, 14(22), 10616. https://doi.org/10.3390/app142210616.
    17. Katsantonis, A., & Katsantonis, I. G. (2024). University Students’ Attitudes toward Artificial Intelligence: An Exploratory Study of the Cognitive, Emotional, and Behavioural Dimensions of AI Attitudes. Education Sciences, 14(9), 988. https://doi.org/10.3390/educsci14090988.
    18. Lee, H. S., & Lee, J. (2021). Applying Artificial Intelligence in Physical Education and Future Perspectives. Sustainability, 13(1), 351. https://doi.org/10.3390/su13010351.
    19. Li, S., Wang, C., & Wang, Y. (2024). Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence. Scientific Reports, 14, 4788. https://doi.org/10.1038/s41598-024-53177-y.
    20. Li, Z., Wang, H., & Saravanan, V. (2020). The effectiveness of physical education teaching in college based on artificial intelligence methods. Journal of Intelligent & Fuzzy Systems, 40(2), 3301–3311. https://doi.org/10.3233/JIFS-189370.
    21. Munk, M., & Agergaard, S. (2018). Listening to students’ silences – a case study examining students’ participation and non-participation in physical education. Physical Education and Sport Pedagogy, 23(4), 371–386. https://doi.org/10.1080/17408989.2018.1441393.
    22. Nadarzynski, T., Miles, O., Cowie, A., & Ridge, D. (2019). Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digital Health, 5. https://doi.org/10.1177/2055207619871808.
    23. Pesovski, I., Santos, R., Henriques, R., & Trajkovik, V. (2024). Generative AI for Customizable Learning Experiences. Sustainability, 16(7), 3034. https://doi.org/10.3390/su16073034.
    24. Putra, G. N. E., & Dendup, T. (2020). Health and behavioural outcomes of bullying victimisation among Indonesian adolescent students: findings from the 2015 Global School-based Student Health Survey. Psychology, Health & Medicine, 27(3), 513–527. https://doi.org/10.1080/13548506.2020.1826546.
    25. Reddy, S. (2024). Generative AI in healthcare: An implementation science informed translational path on application, integration and governance. Implementation Science, 19, 27. https://doi.org/10.1186/s13012-024-01357-9
    26. Sathiparan, N., Jeyananthan, P., & Subramaniam, D. N. (2024). Surface response regression and machine learning techniques to predict the characteristics of pervious concrete usi.ng non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity. Measurement, 225, 114006. https://doi.org/10.1016/j.measurement.2023.114006.
    27. Song, X. (2024). Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network. Scientific Reports, 14, 4104. https://doi.org/10.1038/s41598-024-53964-7.
    28. Tan, M., Xiao, Y., Jing, F., Xie, Y., Lu, S., Xiang, M., & Ren, H. (2024). Evaluating machine learning-enabled and multimodal data-driven exercise prescriptions for mental health: A randomized controlled trial protocol. Frontiers in Psychiatry, 15, Article 1352420. https://doi.org/10.3389/fpsyt.2024.1352420.
    29. Wang, Y., & Wang, X. (2024). Artificial intelligence in physical education: Comprehensive review and future teacher training strategies. Frontiers in Public Health, 12, Article 1484848. https://doi.org/10.3389/fpubh.2024.1484848.
    30. Willetts, M., Atkins, A. S., & Stanier, C. (2022). Quantitative study on barriers of adopting big data analytics for UK and Eire SMEs. In N. Sharma, A. Chakrabarti, V. E. Balas, & A. M. Bruckstein (Eds.), Data management, analytics, and innovation (Vol. 71, pp. 311–324). Lecture Notes on Data Engineering and Communications Technologies. Springer. https://doi.org/10.1007/978-981-16-2937-2_23.
    31. Yang, D., Oh, E.-S., & Wang, Y. (2020). Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot. Sustainability, 12(19), 8000. https://doi.org/10.3390/su12198000.
    32. Yangsheng, Z., & Saravanan, V. (2020). An AI-based design of student performance prediction and evaluation system in college physical education. Journal of Intelligent & Fuzzy Systems, 40(2), 3271–3279. https://doi.org/10.3233/JIFS-189367.
    33. Zha, H., Li, W., Wang, W., & Xiao, J. (2025). The Paradox of AI Empowerment in Primary School Physical Education: Why Technology May Hinder, Not Help, Teaching Efficiency. Behavioral Sciences, 15(2), 240. https://doi.org/10.3390/bs15020240.
    34. Zhang, B., Jin, H., & Duan, X. (2022). Physical education movement and comprehensive health quality intervention under the background of artificial intelligence. Frontiers in Public Health, 10, Article 947731. https://doi.org/10.3389/fpubh.2022.947731.
    35. Zhou, T., Wu, X., Wang, Y., & et al. (2024). Application of artificial intelligence in physical education: A systematic review. Education and Information Technologies, 29, 8203–8220. https://doi.org/10.1007/s10639-023-12128-2.

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  • How to Cite

    Zhang, X., Saidon , M. K. ., & Kamaruddin, 'Aisyah B. . (2025). Research on AI Interaction to Promote Junior School Students’ Interest in ‎Physical Learning and Health Behaviors: A Big Data Analysis. International Journal of Basic and Applied Sciences, 14(5), 216-223. https://doi.org/10.14419/jkh57464