Work

Design and application of art creation education system based on generative adversarial network

Year: 2025

Type: article

Abstract: In response to the limitations of traditional art education, this study proposes a generative adversarial network-based system for supporting artistic creation and teaching. A novel Hierarchical Attention GAN (HAGAN) model was designed and evaluated on a curated dataset of 5000 artworks across various styles. Quantitative metrics (SSIM 0.8325, PSNR 31) and human feedback from 60 participants confirmed HAGAN’s superior quality and diversity over DCGAN and Variational Autoencoder (VAE). Students rated generated works highly in creative inspiration (avg. 4.125), while teachers affirmed their value in instructional support (avg. 4.225). Surveys and Likert-scale protocols ensured reliable subjective evaluation. Although promising, real-world deployment challenges such as device compatibility, computational load, and network access are discussed as key limitations. Future work will address scalability and integration into diverse classroom environments.

Source: Discover Artificial Intelligence

DOI: 10.1007/s44163-025-00682-2

Authors: Xiaoxiao Shi, Yang Yu

Institution: Sehan University

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $1,590