Exploring bias in artificial intelligence: stereotypes and gendered narratives in digital imagery of early childhood educators

Generative artificial intelligence (GenAI) has revolutionized many aspects of our daily lives, including how we interact with and perceive media. GenAI's capability to generate images, especially AI models like DALL-E, has opened new avenues for visual representation. However, these technological advancements are deeply intertwined with societal norms and narratives, particularly concerning gendered roles and professions. Content analysis was utilized to identify patterns in AI-generated images of early childhood educators and leaders, including the representation of gender and other characteristics. Through a feminist post-structuralist theoretical approach, this paper seeks to understand whether AI perpetuates existing stereotypes or offers a medium to challenge and redefine them. Findings suggest that generative AI reinforces existing stereotypes associated with gender in professional roles, portraying early childhood educators and leaders in biased ways that conform to traditional stereotypes. This study adds to the broader literature about the impact of GenAI in reinforcing or dismantling stereotypes in early childhood education.