BehaviorDiff: a VAE–diffusion framework for AI-generated synthetic behavioral data in procrastination prediction for sports instruction

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 (p = 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.