Transforming Art Creation: Leveraging AI-Driven Generative Adversarial Networks in Educational Support Systems

The study evaluates a novel GAN-based educational system’s output quality and responsiveness compared to four baseline architectures—Baseline-GAN, StyleGAN, Pix2Pix, and CycleGAN. Using Fréchet Inception Distance (FID) and Inception Score (IS) metrics, the proposed model excels with a FID of 34.2 and an IS of 3.9, outperforming competitors in both visual fidelity and diversity. User surveys indicated high satisfaction, with average scores for realism and style consistency rating at 4.7 and 4.8, respectively. An ablation study confirmed the importance of input components, revealing that each modality significantly contributes to performance. Additionally, the model achieved a low inference latency of 278ms, supporting real-time classroom applications. However, the system faces limitations, such as reliance on quality training data, potential cultural biases, and sensitivity to input quality. To enhance inclusivity, strategies such as dataset expansion and cultural personalization are recommended. Overall, the GAN-based system shows promise in enriching art education while fostering learner engagement.

Source link