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Artificial Intelligence (AI)-Driven Data Analytics and Decision-Making Efficiency in Physical Education Programs

DOI: 10.18535/sshj.v9i02.1695· Pages: 7089-7126· Vol. 9, No. 02, (2025)· Published: February 28, 2025
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Abstract

Artificial Intelligence (AI) has become an essential tool in modern education, transforming how data is analyzed and decisions are made. In university physical education programs, AI-driven data analytics is increasingly being used to enhance decision-making efficiency. By leveraging vast amounts of data, AI can provide valuable insights that lead to more informed and effective decisions, ultimately improving the quality of physical education programs.

One of the key benefits of AI-driven data analytics is its ability to process and analyze large datasets quickly and accurately. This capability allows physical education programs to identify trends and patterns that may not be immediately apparent through traditional analysis methods. For example, Ying (2021) found that AI could analyze student performance data to identify areas where students may need additional support, enabling instructors to tailor their teaching methods accordingly. This personalized approach leads to better student outcomes and a more efficient use of instructional time.

In addition to improving instructional methods, AI-driven data analytics can also optimize the design and delivery of physical education curricula. Novotný, Marek, and Veselý (2023) highlighted how AI systems could analyze student feedback, participation rates, and academic performance to suggest modifications to the curriculum. By incorporating AI into the curriculum design process, universities can ensure that their physical education programs remain relevant, engaging, and responsive to the needs of students.

Resource allocation is another area where AI-driven data analytics can significantly enhance decision-making efficiency. Zhang and Lu (2020) demonstrated that AI could be used to assess the utilization of sports facilities, equipment, and staffing levels, providing administrators with the insights needed to allocate resources more effectively. By optimizing resource allocation, physical education programs can operate more efficiently, reducing costs while maintaining or even improving the quality of education offered.

Furthermore, AI-driven analytics can play a critical role in student assessment and evaluation. Traditional assessment methods often fail to capture the full range of student abilities and progress, particularly in physical education. According to studies by Horváth and Székely (2022), AI systems can track various performance metrics, such as physical fitness levels and participation rates, to provide a more comprehensive and objective evaluation of student progress. This data-driven approach enables instructors to offer more targeted feedback and support, helping students achieve their full potential.

AI-driven data analytics also facilitates the early identification of at-risk students who may require additional support. Kováč (2023) found that AI could analyze data from multiple sources, such as attendance records, participation levels, and academic performance, to identify students who are disengaged or struggling. Early intervention is crucial in helping these students stay on track and succeed in their physical education courses, contributing to higher retention rates and overall program success.

While the benefits of AI-driven data analytics in university physical education programs are clear, there are also challenges that must be addressed. One significant concern is the ethical implications of using AI to collect and analyze student data. Wu (2020) emphasized the importance of ensuring that AI systems are transparent and that student data is protected to maintain trust and privacy. Universities must develop clear policies and guidelines to address these ethical concerns and ensure the responsible use of AI technology in their physical education programs.

Another challenge is ensuring that educators and administrators are adequately trained to use AI-driven tools effectively. Lang and Král (2021) argued that the successful integration of AI in physical education programs requires not only technical knowledge but also an understanding of how AI can be applied to enhance teaching and learning. Providing ongoing training and support for educators is essential to maximize the potential of AI-driven data analytics and ensure its effective use in decision-making processes.

In conclusion, AI-driven data analytics is transforming decision-making efficiency in university physical education programs. By providing valuable insights into student performance, curriculum design, and resource allocation, AI enables educators and administrators to make more informed and effective decisions. However, addressing ethical concerns and providing adequate training are essential to ensure the responsible and effective use of AI technology. As universities continue to integrate AI into their physical education programs, they have the opportunity to create more personalized, efficient, and impactful educational experiences for their students.

Keywords

Collaborative learningEducationTeaching methodBangladesh College EducationStudent Learning

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Author details
Fuxiang Lyu
1A Dissertation Proposal Presented to the Faculty of the Graduate School EMILIO AGUINALDO COLLEGE Manila, Philippines 2In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY IN EDUCATIONAL MANAGEMENT Major in Physical Education
✉ Corresponding Author
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