Generative adversarial network-based creative synthesis and AI art creation in children's art education: effects on health promotion.

Background: The existing research on children's art education still has shortcomings in the field of artificial intelligence creation support and lacks an exclusive generation model and systematic evaluation framework that takes into account the characteristics of children's cognitive development and physical and mental health needs, which limits the practical application of digital aesthetic education in children's health promotion. Based on the development law of children's aesthetic cognition and the needs of mental health promotion, this study constructs an artificial intelligence (AI) generation model for children's artistic creation. By integrating children's figurative thinking characteristics, creativity development laws and emotional expression needs, it realises accurate support for children's artistic creation process. At the same time, a multi-dimensional evaluation system covering creativity, aesthetic literacy, mental health, visual health and social development is established to systematically evaluate the impact of AI-assisted aesthetic education on children's comprehensive health development so as to provide a theoretical basis and practical reference for children's health-orientated digital aesthetic education model. Methods: From July 2025 to December 2025, 120 children aged 6-14 (divided into three developmental levels: 6-8, 9-11, and 12-14) were selected for a 12-week controlled intervention experiment, and the experimental group (AI man-machine collaborative aesthetic education, using the optimised children-specific improved WGAN-GP creative synthesis model) was set up. In this study, a quasi-experimental design was adopted, and the intervention effect was inferred by the correlation between groups and the changes within groups, without strict causal attribution. In the statistical analysis, age is taken as a covariate into the ANCOVA model, and hierarchical subgroup analysis is carried out according to age levels to test the potential influence of age on the main results. Multidimensional statistical methods such as the independent sample Results: Compared with the control group, the Fréchet dIStance (FID) of the experimental group is 21.15 ± 1.31, the inception score (IS) is 2.87 ± 0.16, and the anti-loss convergence rate is increased by 29.7%, so the model has excellent stability. The results of interpersonal intervention showed that the creativity fluency (79.68 ± 5.82), flexibility (77.35 ± 5.41), and uniqueness (75.46 ± 5.73) of the experimental group were significantly better than those of the control group (67.52 ± 5.13, 65.94 ± 4.87, and 63.15 ± 5.23). After controlling the age covariate, the difference between groups remained significant ( Conclusion: This study shows that the optimized creative synthesis model of the generation confrontation network can adapt to children's artistic creation thinking, and the improvement of creative thinking and aesthetic quality is observed in both AI-assisted creation and children's independent creation, accompanied by the reduction of anxiety, psychological pressure, and visual fatigue and the improvement of social expression level. In view of the quasi-experimental design and limited sample size of this study, the above results should be understood as intervention correlation rather than strict causal effect, and the performance of works under AI-assisted conditions should not be directly equivalent to children's independent aesthetic ability. This study provides a preliminary empirical basis for the scientific and standardized development of digital children's health aesthetic education.