<p>Accurate prediction of procrastination remains a critical challenge in sports instruction, where behavioral data are often limited and constrained by privacy concerns. This study investigates whether AI-generated synthetic data can approximate real-world behavioral patterns to predict procrastination under controlled experimental conditions. A three-stage framework was developed. First, a multimodal dataset (MAP-487) was constructed from 487 university athletes, integrating IMU-derived physiological signals, training adherence records, and psychological self-reports. Second, a hybrid Variational Autoencoder–Diffusion model (BehaviorDiff) was trained to generate synthetic sequences that preserve key statistical and temporal characteristics of the original data. Third, identical BiLSTM–Transformer models were trained and evaluated on real and synthetic datasets using consistent preprocessing, feature space, and train–test splits to ensure comparability. Results show that models trained on synthetic data achieve performance comparable to those trained on real data (accuracy: 0.94 vs 0.91), with the observed difference not reaching statistical significance (<i>p</i> = 0.072). Additional analyses, including correlation-based validation, indicate that synthetic data preserves essential behavioral relationships and predictive structure. The observed performance differences are interpreted as a consequence of smoothing and regularization effects introduced during data generation rather than an increase in informational content. The findings suggest that synthetic data can serve as a privacy-preserving and structurally consistent complement to real-world data for behavioral modeling in sports instruction. However, conclusions are limited to controlled experimental settings, and further validation across independent datasets is required to establish generalizability.</p>