Ning Luo

Challenging Gender Stereotypes in Visual Arts Education through Generative AI: A Critical Feminist Approach

Ning Luo – The Education University of Hong Kong

Abstract:

This study combined generative AI technology with feminist pedagogy to empower visual arts students to explore and dismantle traditional gender norms embedded within classic artworks. The study adopted a mixed-methods approach with 46 master’s-level visual arts students as participants. They were asked to select a classic artwork embedded with gender stereotypes and to write a prompt for the generative AI tool to produce an image that challenges or reverses those stereotypes. They then evaluated the AI-generated image regarding how effectively it addressed or subverted the identified gender norms. To assess participants’ perceptions of AI in their creative practice, the Technology Acceptance Model (TAM) was deployed, providing quantitative data on perceived usefulness, ease of use and attitudes toward AI. Additionally, reflective commentaries were collected to offer qualitative insights into the effectiveness of AI-generated art in challenging gender stereotypes. Through a critical examination of historically significant artworks that reinforce gendered representations, participants generated AI-based visual responses designed to subvert these norms. This process cultivates not only creative innovation but also deep inquiry into the social and cultural forces shaping gender perceptions in art. By harnessing the capabilities of AI, the study explored the potential of technology as a transformative tool for social change in the arts, aligning with feminist pedagogical objectives to promote inclusivity and equity.