Abstract
Generative artificial intelligence (GenAI) has reshaped daily life, influencing how we create and engage with visual media. Tools like DALL-E offer new possibilities for visual representation; however, these advances are embedded within societal norms that often perpetuate gender roles and stereotypes. This paper examines the portrayal of early childhood educators in AI-generated images, a profession that is highly feminised and historically influenced by gender stereotypes. Drawing on Butler’s theory of performativity, this study explores whether GenAI reinforces these stereotypes or opens opportunities to challenge them. Analysis of AI-generated images reveals that GenAI frequently replicates traditional gendered portrayals of early childhood educators, reflecting existing societal biases. These findings suggest that GenAI, rather than acting as a neutral tool, perpetuates stereotypes embedded in its training data, particularly within professions marked by established gender norms. This study advocates for more intentional GenAI design to prevent reinforcing stereotypes, proposing that with thoughtful development, such technology could challenge conventional narratives and promote inclusive representation across professions. This research contributes to understanding GenAI’s dual role in reinforcing or challenging stereotypes in early childhood education. It also emphasises the dynamic interaction between human learning and machine learning in shaping equitable educational spaces, aligning with the theme of the Thirty-Second International Conference on Learning. By examining how human-supplied data influences GenAI’s outputs and how machine learning can reshape societal perceptions, this paper addresses both the challenges and opportunities GenAI presents for creating a more inclusive future.
Presenters
Eloise ThomsonAssociate Professor/Head of Program, Education, Melbourne Polytechnic, Victoria, Australia
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Gender, Generative Artificial Intelligence, Performativity, Early Childhood, Leadership, Stereotypes
