AI-Supported and Traditional Tennis Training in Individuals With Intellectual Disabilities

NCT07577830 | Completed

• 3 other identifiers: 2026/29441183-YL-25Approval No: E-84459573-605-17
• Study Type: interventional
• Target: 30
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to examine the tennis learning experiences of children with mild intellectual disabilities aged 12-18 years by comparing a real-time pose recognition technology-supported teaching approach with a traditional face-to-face instructional method. The research focuses on how participants experience these two different teaching approaches and how these approaches influence their learning process in basic tennis skills. A total of 30 participants will be included and divided into two groups: one group will receive tennis instruction supported by real-time pose recognition technology, and the other group will receive traditional instructor-led training. The intervention will last for 14 weeks and will focus on teaching basic tennis skills such as forehand and backhand strokes. The study seeks to answer the following questions: How do children with mild intellectual disabilities experience technology-supported versus traditional tennis instruction? What differences exist between the two approaches in terms of learning experience, engagement, and motor skill development?

Conditions

Intellectual Disability

Keywords

Intellectual Disability; Tennis Training; Motor Skill Acquisition; Physical Education; Technology-Assisted Instruction; Pose Estimation; Traditional Instruction

Outcome Measures

Primary Outcomes (1)

• Learning Experience During Tennis Instruction

  Participants' learning experiences during tennis skill acquisition (forehand and backhand) will be assessed through semi-structured interviews. The primary outcome is the qualitative description of how participants with mild intellectual disabilities perceive technology-supported and traditional tennis instruction in terms of ease of learning, understanding of movements, and perceived improvement.

  Week 7, Week 14, and Week 15

Secondary Outcomes (1)

• Perceived Motor Skill Development

  Week 7, Week 14, and Week 15

Study Arms (2)

Technology-Supported Tennis Training

EXPERIMENTAL

Participants receive tennis instruction supported by real-time pose recognition technology. The system provides immediate visual feedback on body positioning and movement accuracy during forehand and backhand skill acquisition. Instruction focuses on correcting movement patterns and enhancing motor learning through augmented feedback.

Behavioral: Technology-Supported Tennis Training (Real-Time Pose Estimation-Based Instruction)

Traditional Tennis Training

ACTIVE COMPARATOR

Participants receive standard teacher-led tennis instruction without technological assistance. Training includes demonstration, verbal explanation, repetition, and corrective feedback provided directly by the instructor during forehand and backhand skill practice.

Behavioral: Traditional Tennis Training

Interventions

Traditional Tennis Training BEHAVIORAL

Participants receive standard face-to-face tennis instruction without technological support. Teaching includes instructor demonstration, verbal explanation, repetition, and corrective feedback during forehand and backhand skill practice.

Traditional Tennis Training

Technology-Supported Tennis Training (Real-Time Pose Estimation-Based Instruction) BEHAVIORAL

Participants receive tennis instruction supported by a real-time pose recognition system (MediaPipe-based). The system provides immediate visual feedback on body posture and movement accuracy during forehand and backhand skill acquisition. Instruction aims to enhance motor learning through augmented feedback and movement correction.

Technology-Supported Tennis Training

Eligibility Criteria

• Age: 12 Years - 18 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)

You may qualify if:

• Aged 12-18 years
• Diagnosed with mild intellectual disability
• Willing to participate voluntarily in the study
• Able to attend regular training sessions (3 days per week for 14 weeks)
• No severe physical condition preventing participation in physical activity
• Right-hand dominant

You may not qualify if:

• Withdrawal of consent by participant or legal guardian
• Irregular attendance in training sessions or inability to complete the intervention program
• Missing at least one of the scheduled interview sessions
• Presence of severe physical or medical conditions that may limit participation in tennis activities
• Inability to follow basic instructions during training sessions

Sponsors & Collaborators

• Burdur Mehmet Akif Ersoy University: lead

Study Sites (1)

Burdur Mehmet Akif Ersoy University, School of Sport Sciences

Burdur, Burdur, 15200, Turkey (Türkiye)

Location

Related Publications (1)

• Ahuja, N. J., Dutt, S., Choudhary, S. L., & Kumar, M. (2025). Intelligent tutoring system in education for disabled learners using human-computer interaction and augmented reality. International Journal of Human-Computer Interaction, 41(3), 1804-1816. https://doi.org/10.1080/10447318.2022.2124359. Alsolami, A. S. (2025). The effectiveness of using artificial intelligence in improving academic skills of school-aged students with mild intellectual disabilities in Saudi Arabia. Research in Developmental Disabilities, 156, 104884. https://doi.org/10.1016/j.ridd.2024.104884 Chiu, T. K. (2024). The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187-6203. https://doi.org/10.1080/10494820.2023.2253861 Creswell, J. W. (2016). Nitel araştırma yöntemleri: Beş yaklaşıma göre nitel araştırma ve araştırma deseni. Siyasal kitabevi. Google AI. (2020). MediaPipe Iris: Real-time Iris Tracking & Depth Estimation https://ai.googleblog.com/2020/08/mediapipe-iris-real-time-iris-tracking.html sayfasından erişilmiştir. He, Q., Chen, H., & Mo, X. (2024). Practical application of interactive AI technology based on visual analysis in professional system of physical education in universities. Heliyon, 10(3). https://doi.org/10.1016/j.heliyon.2024.e24627 Johnson, D. (2019). Adaptive Learning Systems and Personalized Education. Perspectives in Innovative Education, 1(1), 1-10. Klavina, A., Pérez-Fuster, P., Daems, J., Lyhne, C. N., Dervishi, E., Pajalic, Z., ... & Sousa, C. (2024). The use of assistive technology to promote practical skills in persons with autism spectrum disorder and intellectual disabilities: A systematic review. Digital Health, 10, 20552076241281260. https://doi.org/10.1177/2055207624128126 Kulkarni, K. M., & Shenoy, S. (2021). Table tennis stroke recognition using two-dimensional human pose estimation. In Proceedings of the IEEE/CVF conference on com

  BACKGROUND

MeSH Terms

Conditions

Intellectual Disability

Condition Hierarchy (Ancestors)

Neurobehavioral Manifestations; Neurologic Manifestations; Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Neurodevelopmental Disorders; Mental Disorders

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: The study is designed as a parallel-group intervention in which participants with mild intellectual disabilities are allocated into two independent groups. One group receives tennis instruction supported by real-time pose recognition technology, while the other group receives traditional teacher-led instruction. Both groups undergo the intervention simultaneously over a 14-week period. No crossover between groups occurs during the study, and outcomes are compared between groups at the end of the intervention.

Study Record Dates

• First Submitted: April 29, 2026
• First Posted: May 11, 2026
• Study Start: October 13, 2025
• Primary Completion: January 15, 2026
• Study Completion: January 20, 2026
• Last Updated: May 11, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)