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ZENODO
Journal . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Journal . 2025
License: CC BY
Data sources: Datacite
ZENODO
Journal . 2025
License: CC BY
Data sources: Datacite
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AI-DRIVEN TRAINING CUES FOR IMPROVING STUDENT-ATHLETE SKILL LEARNING

Authors: Dr. Daptare S., Dr. Paneru U. & Dr. Lohote P.;

AI-DRIVEN TRAINING CUES FOR IMPROVING STUDENT-ATHLETE SKILL LEARNING

Abstract

The use of Artificial Intelligence is growing in the field of sports training, and it is slowly becoming a useful support system for Physical Education teachers and student–athletes. This study examined how AI-based training cues, such as simple motion alerts, skill correction messages, and real-time feedback, can help young learners improve their sports techniques. The work also looks at how students respond to these cues in terms of focus, motivation, and confidence during practice sessions. The paper is based on a review of recent developments in AI tools used in training, along with observations from regular Physical Education settings. The findings suggest that AI cues make learning clearer, reduce common errors, and provide more personalised support for each student. This approach can help both teachers and coaches guide skill learning in a better and time-saving manner. Overall, the study shows that AI-driven cues have good potential to improve student–athlete performance and can be used safely and effectively in schools and colleges.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average