ArticleScientific reports2024
Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence for university physical education: a data-knowledge synergy digital-intelligent sports platform.Frontiers in public health · 2026Article
- Bioinspired high-order in-sensor spatiotemporal enhancement in van der Waals optoelectronic neuromorphic electronics.Nature communications · 2025Article
- Physical function evaluation in volleyball training based on intelligent GRNN.Scientific reports · 2025Article
- Evaluating Assistive Technology Outcomes in Boccia Athletes with Disabilities Using AI-Based Kinematic Analysis.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning based multi attribute evaluation for holistic student assessment in physical education.Scientific reports · 2025Article
- The Paradox of AI Empowerment in Primary School Physical Education: Why Technology May Hinder, Not Help, Teaching Efficiency.Behavioral sciences (Basel, Switzerland) · 2025Article
- Artificial intelligence in physical education: comprehensive review and future teacher training strategies.Frontiers in public health · 2024Review
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1 author.
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Abstract
This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.
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Registered trials
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