The Unseen Hand: How User Background Shapes AI Text-to-Image Generation

Text-To-Image (TTI) generators are becoming widely used and are often promoted as "democratizing" the making of images independently of the skill set of the user. But relatively little is known about whether people with different educations, skill sets and literacies use these tools differently, and how their backgrounds influence the quality of the results. In this article we investigate the impact of Visual Literacy (VL), AI Literacy (AIL) and Prompt Engineering Literacy (PEL) on prompt use. More precisely we are examining how they each<br/>impact prompt usage patterns, linguistic and semantic composition, and the structure and morphology of prompts. Additionally, we employed Process Analysis to investigate how participants with different literacy<br/>approach the creative design process with TTI. Our results show that individuals scoring high on Visual Literacy (VL) tend to employ a larger vocabulary and more nuanced language, greater prompt variety and make more references to art genres, styles, and movements. In contrast, participants scoring high AI Literacy (AIL) demonstrated a deeper understanding of AI interaction, influencing their prompting strategies, and tended to adhere closely to the exact wording of the brief, treating it as precise specifications. The study's findings have the potential of significantly impacting educations, both by actively teaching students to experiment with prompt variations and deepen expressive visual vocabulary as well as designing curricula that incorporate structured exercises on iterative prompt refinement, explicitly teaching how to adjust prompts based on AI<br/>output to achieve desired visual outcomes.