Generative adversarial network-based creative synthesis and AI art creation in children's art education: effects on health promotion.
- 1College of the Arts Hebei Agricultural University, Baoding, China.
Abstract
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 t-test, paired sample t-test, two-factor repeated measurement variance analysis, one-way covariance analysis (ANCOVA) with age as a covariate, Pearson correlation analysis, intra-group correlation coefficient (ICC), standardised mean difference (SMD), and determination coefficient (r) were used. Through the Torrance Creative Thinking Test (TTCT), the self-made Children's Artistic Aesthetic Literacy Rating Scale, which has been systematically developed and verified by reliability and validity, the generated image quality evaluation system, the Children's Depression and Anxiety Scale (SCARED), the Children's Perceived Stress Scale (CPSS), the visual fatigue score, the children's social expression health scale, the naked eye vision difference and the resting heart rate, the multi-dimensional indicators, the observed image quality evaluation system was generated. All health scales are marked with the applicable age range, and the standardised testing procedures of picture-assisted and item-by-item reading are adopted for children aged 6-8, and the boundaries of adult assistance are defined.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 (f value ranged from 87.32 to 156.48, all p < 0.001), and the main effect of age was significant in some indexes but did not change the intervention direction. The scores of aesthetic perception, color matching, and composition creativity in the experimental group (84.36 ± 3.81) were significantly higher than those in the control group (66.28 ± 4.53), in which the scores of works with AI assistance and works without AI were reported separately to distinguish the quality of AI-assisted output from children's independent aesthetic ability. The scores of anxiety (12.35 ± 2.11), perceived stress (9.62 ± 1.87), and visual fatigue (2.14 ± 0.63) in the experimental group were significantly lower than those in the control group (21.76 ± 3.42, 17.53 ± 2.69, and 4.89 ± 0.92). The fluctuation difference of naked-eye vision after class was 0.12/0.05 in the experimental group and 0.25/0.08 in the control group (p < 0.01). 5 mins after class, the resting heart rate was 77.93 ± 5.26 beats/min in the experimental group and 86.71 ± 6.22 beats/min in the control group (p < 0.01). The score of social health expression (87.41 3.65) was significantly higher than that of the control group (64.33 4.17), and the difference of all health indicators among groups was p < 0.001. The score consistency within the group was ICC = 0.84 (p < 0.001), and the intervention effect amount SMD = 0.68, which was a large-effect positive intervention. Pearson correlation analysis shows that children's participation in AI creation is highly positively correlated with the quality of creative output (r = 0.812, p < 0.001), which is a cross-sectional correlation and does not constitute causal inference. Tool familiarity, personal preference, and exploration behavior may be potential confounding factors. Stratified subgroups showed that children with high anxiety and stress at baseline had the greatest improvement.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…
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BibTeX
@article{chen2026generative,
title = {{Generative adversarial network-based creative synthesis and AI art creation in children's art education: effects on health promotion.}},
author = {Jing Chen and Yang Zhang and Hui Liu},
journal = {Frontiers in public health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1945238},
url = {https://europepmc.org/article/MED/42910067},
note = {CC BY},
}
RIS
TY - JOUR
TI - Generative adversarial network-based creative synthesis and AI art creation in children's art education: effects on health promotion.
AU - Jing Chen
AU - Yang Zhang
AU - Hui Liu
PY - 2026
DA - 2026/10/07
JO - Frontiers in public health
DO - 10.3389/fpubh.2026.1945238
UR - https://europepmc.org/article/MED/42910067
AB - 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 t-test, paired sample t-test, two-factor repeated measurement variance analysis, one-way covariance analysis (ANCOVA) with age as a covariate, Pearson correlation analysis, intra-group correlation coefficient (ICC), standardised mean difference (SMD), and determination coefficient (r) were used. Through the Torrance Creative Thinking Test (TTCT), the self-made Children's Artistic Aesthetic Literacy Rating Scale, which has been systematically developed and verified by reliability and validity, the generated image quality evaluation system, the Children's Depression and Anxiety Scale (SCARED), the Children's Perceived Stress Scale (CPSS), the visual fatigue score, the children's social expression health scale, the naked eye vision difference and the resting heart rate, the multi-dimensional indicators, the observed image quality evaluation system was generated. All health scales are marked with the applicable age range, and the standardised testing procedures of picture-assisted and item-by-item reading are adopted for children aged 6-8, and the boundaries of adult assistance are defined.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 (f value ranged from 87.32 to 156.48, all p < 0.001), and the main effect of age was significant in some indexes but did not change the intervention direction. The scores of aesthetic perception, color matching, and composition creativity in the experimental group (84.36 ± 3.81) were significantly higher than those in the control group (66.28 ± 4.53), in which the scores of works with AI assistance and works without AI were reported separately to distinguish the quality of AI-assisted output from children's independent aesthetic ability. The scores of anxiety (12.35 ± 2.11), perceived stress (9.62 ± 1.87), and visual fatigue (2.14 ± 0.63) in the experimental group were significantly lower than those in the control group (21.76 ± 3.42, 17.53 ± 2.69, and 4.89 ± 0.92). The fluctuation difference of naked-eye vision after class was 0.12/0.05 in the experimental group and 0.25/0.08 in the control group (p < 0.01). 5 mins after class, the resting heart rate was 77.93 ± 5.26 beats/min in the experimental group and 86.71 ± 6.22 beats/min in the control group (p < 0.01). The score of social health expression (87.41 3.65) was significantly higher than that of the control group (64.33 4.17), and the difference of all health indicators among groups was p < 0.001. The score consistency within the group was ICC = 0.84 (p < 0.001), and the intervention effect amount SMD = 0.68, which was a large-effect positive intervention. Pearson correlation analysis shows that children's participation in AI creation is highly positively correlated with the quality of creative output (r = 0.812, p < 0.001), which is a cross-sectional correlation and does not constitute causal inference. Tool familiarity, personal preference, and exploration behavior may be potential confounding factors. Stratified subgroups showed that children with high anxiety and stress at baseline had the greatest improvement.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…
SN - 2296-2565
ER -