Enhancing art creation through AI-based generative adversarial networks in educational auxiliary system

Creative art education increasingly demands interactive, personalized tools that support students in developing aesthetic expression and technical skill. Traditional digital art tools often lack adaptive feedback and require manual intervention, which limits their pedagogical potential in self-guided or remote learning environments. This study introduces an AI-enhanced educational auxiliary system powered by Generative Adversarial Networks (GANs) to support art creation, creativity development, and learning engagement. The proposed system integrates a hybrid GAN architecture that enables semantic sketch-to-image transformation, style transfer, and real-time visual feedback based on user input. Students can co-create digital artwork with the system, which dynamically learns their preferences and evolves to provide constructive artistic suggestions and inspirations. The GAN model is trained on curated datasets of historical and contemporary art styles, enabling it to emulate diverse visual aesthetics. Evaluation on a cohort of 60 undergraduate art students showed a 35.4% improvement in creative output quality, as judged by expert reviewers, and a 42.7% increase in engagement over traditional tools. The system also offers explainable visual outputs that foster reflection and critique. This work provides a scalable AI-assisted learning framework that enhances artistic exploration while preserving creative autonomy in educational settings.