Implementation of Artificial Intelligence-Assisted Learning to Optimize Biomechanical Techniques and Decision Making Skills in Basketball Training for Young Athletes
DOI:
https://doi.org/10.37742/jmpo.v6i2.176Keywords:
artificial intelligence, biomechanics, decision making, basketball, young athletesAbstract
This study analyzes the effectiveness of AI-assisted learning implementation in optimizing biomechanical techniques and decision-making skills of young basketball players. Using a quasi-experimental design with a pretest-posttest control group, the study involved 40 Bandung Regency basketball club athletes (aged 14-17 years) through a total sampling technique. The research instruments used the Biomechanical Assessment Protocol (reliability 0.91) and the Basketball Decision-Making Test (reliability 0.88). The experimental group showed significant improvements in biomechanical techniques from M=64.35 (SD=7.84) to M=82.45 (SD=5.67), t(19)=-15.23, p<0.001, and decision-making skills from M=61.80 (SD=8.92) to M=80.15 (SD=6.34), t(19)=-13.87, p<0.001. The posttest difference between the experimental and control groups was highly significant (p<0.001) with Cohen's d > 1.5. AI-assisted learning was shown to be effective in optimizing the biomechanical technique and decision-making skills of young basketball players with a very large effect size
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