Animating Learning: Effects of AI-Based 3D Visual Feedback on Tackling Skill Acquisition, Motivation, and Performance in Youth Soccer

Abstract

Purpose: The integration of visual feedback (VFB) has become essential in physical education (PE), particularly with the
emergence in artificial intelligence (AI) technologies. However, tackling techniques in youth soccer training have received
limited empirical attention, even though they represent a complex and high-risk defensive skill. The purpose of the study
was to analyse the effects of AI-enhanced VFB, using 3D animated motion capture, on the acquisition of tackling skills among
youth soccer players.
Methods: Twenty-three participants were randomly assigned to an experimental group (EG; n = 12), utilizing AI-animated
VFB, or to a control group (CG; n = 11) which received only verbal feedback. Technical performance was evaluated through
four validated success criteria at the 1st, 6th, and 12th training lessons, while biomechanical efficiency was measured by
comparing the trunk-leg angle to a model angle (167.3°). Motivation was evaluated using the Situational Motivation Scale.
Results: The findings show that the EG achieved a significantly greater improvement in technical performance (+179%)
compared to the CG (+96%), with very marked effects for leg-knee positioning (Criteria 3) and ball contact (Criteria 4).
The leg-trunk angle of the EG also improved significantly (+23.84%), approaching the expert model, while the CG showed
only a modest increase (+9.63%). Furthermore, intrinsic motivation increased significantly in the EG (P = .001; d = 1.44),
accompanied by a significant reduction in amotivation (P = .004; d = 1.07).
Conclusions: The AI-enhanced VFB allowed players to analyse their performance through 3D animations, facilitating skill
improvement. These findings highlight the potential of AI-driven visual feedback in sports training, through further research
needed to optimize its application in skill acquisition.