Abstract
This work explores the multidimensional impacts of an Artificial Intelligence (AI) environment on Taekwondo training for middle school students. It establishes an intelligent training system and personalized training programs and compares the training outcomes of an experimental group (trained in an AI environment) with a control group (trained in a traditional environment). All participants are from the same middle school and undergo baseline assessments before the study to ensure data reliability and consistency. The results indicate that psychological state significantly and positively impacts students’ motivation levels (supporting Hypothesis 1), meaning that a good psychological state can markedly enhance middle school students’ training motivation. Additionally, motivation levels have a notable positive effect on the performance of technical movements (supporting Hypothesis 2). This illustrates that higher motivation levels can effectively improve the quality of technical movements, highlighting the importance of motivation in training outcomes. Furthermore, self-efficacy also has a significant positive influence on technical movement scores (supporting Hypothesis 3), indicating that the higher the students’ confidence in their abilities is, the better their technical performance is. The impact of training records on technical movement scores is likewise significant (supporting Hypothesis 4), where more training time and higher engagement can effectively enhance technical scores, emphasizing the importance of behavioral involvement. Finally, motivation levels also have a significant positive effect on self-efficacy (supporting Hypothesis 5), with high motivation levels contributing to an increase in students’ self-efficacy.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author Roxana Dev Omar Dev on reasonable request via e-mail rdod@upm.edu.my.
References
Tan, L. & Ran, N. Applying Artificial Intelligence Technology to Analyze the Athletes’ Training Under Sports Training Monitoring System[J]. Int. J. Humanoid Rob. 20 (06), 2250017 (2023).
Mei, Z. 3D image analysis of sports technical features and sports training methods based on artificial intelligence[J]. J. Test. Eval. 51 (1), 189–200 (2023).
Ermakov, A. V., Skarzhynskaya, E. N. & Novoselov, M. A. Digital transformation of professions in physical education and sport sector[J]. Theory Pract. Phys. Cult., No.3(2022): 7–9. (2022).
Lansang, N. M. Effectiveness of Taekwondo in Improving Postural Control among Healthy Adolescents: a Systematic Review[J]. Arch. Phys. Med. Rehabil. 103 (12), e207 (2022).
Park, J. et al. Comparison of Physique and Physical Fitness Factor Characteristics of College Taekwondo Majors by School Year[J]. Kinesiology 7 (1), 1–10 (2022).
Heydari, M. et al. Effect of competition on salivary α-amylase in taekwondo athletes[J]. Sci. Sports. 37 (7), 618–623 (2022).
Unalmis, Y. & Muniroglu, S. Examination of the effect of fascial therapy on some physical fitness parameters in taekwondo athletes[J]. Sports Med. Health Sci. 5 (4), 299–307 (2023).
Savić, R. & Kalinović, M. Psychomotor pattern for neurological assessment of reflex in karate and taekwondo: Manual muscle test under load-general model[J]. Sport-nauka i praksa. 12 (1), 9–21 (2022).
Cao, P. et al. Identity Negotiation and Reconstruction Following a Spinal Cord Injury: A Meta-Synthesis of Qualitative Evidence[J]. Arch. Phys. Med. Rehabil. 103 (12), e207 (2022).
Tan, T. C. & Lee, J. W. Technology, innovation, and the future of the sport industry in Asia Pacific[J]. Sport Soc. 26 (3), 383–389 (2023).
Blanco Ortega, A. et al. Biomechanics of the upper limbs: A review in the sports combat ambit highlighting wearable sensors[J]. Sensors 22 (13), 4905 (2022).
Shin, M. C. et al. When Taekwondo Meets Artificial Intelligence: The Development of Taekwondo[J]. Appl. Sci. 14 (7), 3093 (2024).
Su, Z. Artificial intelligence in the auxiliary guidance function of athletes’ movement standard training in physical education[J]. J. circuits Syst. computers. 31 (11), 2240001 (2022).
Kim, J. Y. & Cho, K. C. A Taekwondo Poomsae Movement Classification Model Learned Under Various Conditions[J]. J. Korea Soc. Comput. Inform. 28 (10), 9–16 (2023).
Cui, X. & Hu, R. Application of intelligent edge computing technology for video surveillance in human movement recognition and Taekwondo training[J]. Alexandria Eng. J. 61 (4), 2899–2908 (2022).
Dos Santos, L. M. Learning taekwondo martial arts lessons online: The perspectives of social cognitive career and motivation theory[J]. Int. J. Instruction. 15 (1), 1065–1080 (2022).
Kim, Y. J. et al. The psychosocial effects of Taekwondo training: a meta-analysis[J]. Int. J. Environ. Res. Public Health. 18 (21), 11427 (2021).
Emru Tadesee, M. Martial arts and adolescents: using theories to explain the positive effects of Asian martial arts on the well-being of adolescents[J]. Ido Movement for Culture. J. Martial Arts Anthropol. 17 (2), 9–23 (2017).
Cunha, P. et al. User Assessment of a Customized Taekwondo Athlete Performance Cyber–Physical System[J]. Appl. Sci. 14 (11), 4683 (2024).
Yu, H. et al. Artificial intelligence-based quality management and detection system for personalized learning[J]. J. Interconnect. Networks. 22 (Supp02), 2143004 (2022).
Frei-Landau, R., Orland-Barak, L. & Muchnick-Rozonov, Y. What’s in it for the observer? Mimetic aspects of learning through observation in simulation-based learning in teacher education[J]. Teach. Teacher Educ. 113, 103682 (2022).
Yang, X. & Mojtahe, K. Understanding the Role of Self-Efficacy in Sports Performance: A Longitudinal Study[J]. Revista de Psicología del Deporte. (Journal Sport Psychology). 32 (1), 311–319 (2023).
Hodges, N. J. & Lohse, K. R. An extended challenge-based framework for practice design in sports coaching[J]. J. Sports Sci. 40 (7), 754–768 (2022).
Ryan, R. M. & Deci, E. L. Intrinsic and extrinsic motivations: Classic definitions and new directions[J]. Contemp. Educ. Psychol. 25 (1), 54–67 (2000).
Yuan, X. et al. Time lagged investigation of entrepreneurship school innovation climate and students motivational outcomes: Moderating role of students’ attitude toward technology[J]. Front. Psychol. 13, 979562 (2022).
Balcombe, L. & De Leo, D. Psychological screening and tracking of athletes and digital mental health solutions in a hybrid model of care: mini review[J]. JMIR Formative Res. 4 (12), e22755 (2020).
Diaz-Canestro, C., Siebenmann, C. & Montero, D. Marked improvements in cardiac function in postmenopausal women exposed to blood withdrawal plus endurance training[J]. J. Sports Sci. 40 (14), 1609–1617 (2022).
Seçkin, A. Ç., Ateş, B. & Seçkin, M. Review on Wearable Technology in sports: Concepts, Challenges and opportunities[J]. Appl. Sci. 13 (18), 10399 (2023).
Bucea-Manea-Țoniș, R. et al. Creating IoT-enriched learner-centered environments in sports science higher education during the pandemic[J]. Sustainability 14 (7), 4339 (2022).
Ho, I. M. K. et al. Plyometric stress index: A novel method for quantifying plyometric training[J]. Sci. Sports. 37 (8), 788–797 (2022).
Singh, U. et al. Jump rope training effects on health-and sport-related physical fitness in young participants: A systematic review with meta-analysis[J]. J. Sports Sci. 40 (16), 1801–1814 (2022).
Zhang, X. & Ma, C. Intelligent Development of College Physical Education Teaching Mode Based on Internet+[J]. Int. J. Web-Based Learn. Teach. Technol. (IJWLTT). 19 (1), 1–13 (2024).
Cai, H. Application of intelligent real-time image processing in fitness motion detection under internet of things[J]. J. Supercomputing. 78 (6), 7788–7804 (2022).
Yilmaz, R. & Yilmaz, F. G. K. Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning[J]. Computers Hum. Behavior: Artif. Hum. 1 (2), 100005 (2023).
Yilmaz, R. & Yilmaz, F. G. K. The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation[J]. Computers Education: Artif. Intell. 4, 100147 (2023).
Yılmaz, G. K. et al. The Smart MOOC Integrated with Intelligent Tutoring: A Case Study[M]//Open and Inclusive Educational Practice in the Digital World 15–27 (Springer International Publishing, 2022).
Yilmaz, R. et al. Smart MOOC integrated with intelligent tutoring: A system architecture and framework model proposal[J]. Computers Education: Artif. Intell. 3, 100092 (2022).
Yılmaz, R. Yılmaz F G K. Examining student satisfaction with the use of smart mooc[C] (International İstanbul Scientific Research Congress, 2022).
Lu, B. Intelligent control system of physical strength in sports based on independent component analysis[J]. Neural Comput. Appl. 35 (6), 4397–4408 (2023).
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YuBin Yuan: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation Roxana Dev Omar Dev: writing—review and editing, visualization, supervision, project administration, funding acquisitionKim Geok Soh : software, validation, formal analysisXueYan Ji: visualization, supervision Fangyi Li: formal analysis, investigation, resources, data curation.
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The studies involving human participants were reviewed and approved by Ethic Committee for Research Involving Human Subject (JKEUPM), Universiti Putra Malaysia (Approval Number: 2022.49485966). The participants provided their written informed consent to participate in this study. All methods were performed in accordance with relevant guidelines and regulations.
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Yuan, Y., Omar Dev, R., Soh, K.G. et al. An empirical study on the multidimensional influencing factors of taekwondo training for middle school students in an artificial intelligence environment. Sci Rep (2026). https://doi.org/10.1038/s41598-026-49368-4
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DOI: https://doi.org/10.1038/s41598-026-49368-4
