Scientific Reports Article
An empirical study on the multidimensional influencing factors of taekwondo training for middle school students in an artificial intelligence environment
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 Al 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.
Executive Impact: Key Performance Indicators
AI-assisted Taekwondo training significantly boosts student performance and engagement across critical dimensions.
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AI Model Accuracy
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Training Persistence Increase
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Technical Skill Improvement
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Student Satisfaction
Deep Analysis & Enterprise Applications
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AI Impact on Technical Skills
This work explores how AI technologies enhance Taekwondo training techniques for middle school students, focusing on improvements in movement accuracy, reaction time, and technical consistency through video analysis, action recognition, and personalized recommendations. The AI system offers precise technical feedback and optimizes students' training experiences. Through comparative analysis, the experimental group showed remarkable improvements in Taekwondo specialized skills and techniques, significantly outperforming the control group in areas such as high roundhouse kicks, double flying kicks, 360° round kick, spinning back kick, and poomsae.
Specialized Taekwondo Skill Improvement
| Technique | Experimental Group (Mean) | Control Group (Mean) | P-value | Effect Size (Cohen's d) |
|---|---|---|---|---|
| High Roundhouse Kicks | 34.39 | 32.89 | <0.01 | 0.46 |
| Double Flying Kicks | 54.89 | 52.64 | <0.01 | 1.51 |
| 360° Round Kick | 9.32 | 8.62 | <0.01 | 3.18 |
| Spinning Back Kick | 8.87 | 8.36 | <0.01 | 3.92 |
| Poomsae | 8.78 | 8.27 | <0.01 | 2.83 |
Training Persistence & Motivation
The study finds that AI-enhanced environments significantly boost student training persistence. The experimental group exhibited markedly higher attendance rates, longer training durations, and greater training continuity compared to the control group. SEM analysis confirms that psychological state significantly and positively impacts motivation levels, which in turn positively affects training persistence. This highlights AI's role in providing real-time feedback and personalized guidance, fostering sustained engagement.
18%
Increase in Attendance Rate with AI-Assisted Training
12
Months Average Training Continuity in AI Group
Psychological Factors & Self-Efficacy
AI technology positively influences middle school students' psychological states and motivation levels, enhancing confidence and a sense of achievement. SEM results confirm that a good psychological state significantly impacts motivation, and motivation, in turn, significantly influences self-efficacy. Higher motivation levels lead to increased confidence in training outcomes, demonstrating AI's ability to provide personalized feedback and incentive mechanisms that support students' mental well-being and performance.
AI's Role in Boosting Self-Efficacy
The research confirmed that motivation levels significantly impact self-efficacy (supporting Hypothesis 5). This means that when students are highly motivated, their confidence in their abilities to complete tasks increases, leading to better technical performance. The AI environment, with its personalized feedback and incentive mechanisms, is crucial in fostering this positive cycle. By providing real-time, constructive feedback and recognizing progress, AI helps students build confidence and maintain a positive psychological state, directly influencing their perceived self-efficacy and overall training success.
AI System Design & Methodology
This research developed an AI-assisted Taekwondo training system for middle school students. The system integrates machine learning algorithms for motion analysis, personalized training plans, and real-time feedback. Data collection involves multimodal sensors and high-definition cameras to capture kinematic and physiological data. A hybrid SVM+CNN model ensures high-precision recognition of actions and accurate score prediction, adapting dynamically to athlete performance and optimizing training content.
Enterprise Process Flow
Athlete Data Collection (Sensors, Cameras)
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Data Processing & Analysis (ML Algorithms)
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Personalized Training Plan Generation
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Real-time Feedback & Adjustments
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Performance Evaluation & Tracking
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Your AI Implementation Roadmap
A structured approach to integrating AI into your Taekwondo training programs, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Strategy
Conduct a comprehensive assessment of current training methods, identify key areas for AI integration, and define specific performance and psychological goals. Develop a tailored AI strategy document.
Phase 2: System Integration & Customization
Implement the AI-assisted training platform, integrate with existing infrastructure (e.g., motion capture systems), and customize personalized training programs based on student data and psychological profiles.
Phase 3: Pilot Program & Feedback
Launch a pilot AI-enhanced training program with a select group of students. Collect detailed performance and psychological data, gather feedback from students and coaches, and iterate on system features.
Phase 4: Full Deployment & Continuous Optimization
Roll out the AI system to all relevant student groups. Establish ongoing monitoring, data analysis, and A/B testing protocols to continuously optimize training plans, feedback mechanisms, and motivational strategies.
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