Abstract
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. However, relatively little is known about whether people with different educational backgrounds, skill sets, and literacies use these tools differently, or how these 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 examine how each literacy influences 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 profiles approach the creative design process when using TTI systems. Our results show that individuals scoring high on Visual Literacy (VL) tend to employ a larger vocabulary and more nuanced language, demonstrate greater prompt variety, and make more references to art genres, styles, and movements. In contrast, participants scoring high in AI Literacy (AIL) demonstrated a deeper understanding of AI interaction, which influenced their prompting strategies; they tended to adhere closely to the exact wording of the brief, treating it as a set of precise specifications. The study’s findings have the potential to significantly impact education, both by encouraging students to experiment with prompt variations and deepen their expressive visual vocabulary, and by informing the design of curricula that incorporate structured exercises in iterative prompt refinement, explicitly teaching how to adjust prompts based on AI output to achieve desired visual outcomes.
Keywords
Text-to-Image, Generative AI, User Study, Visual Literacy, AI Literacy, Prompt Engineering Literacy, Prompt analysis, Process analysis, Technical Education, Design Education
DOI
https://doi.org/10.21606/iasdr.2025.1177
Citation
Canossa, A., Toender, L., Fellner, L., Juul, J., Maden, W.V.,and Zhu, J.(2025) The Unseen Hand: The Influence of Visual and AI Literacy on AI Text-to-Image Generation, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.1177
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 3 - Design, Art & Technology
The Unseen Hand: The Influence of Visual and AI Literacy on 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. However, relatively little is known about whether people with different educational backgrounds, skill sets, and literacies use these tools differently, or how these 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 examine how each literacy influences 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 profiles approach the creative design process when using TTI systems. Our results show that individuals scoring high on Visual Literacy (VL) tend to employ a larger vocabulary and more nuanced language, demonstrate greater prompt variety, and make more references to art genres, styles, and movements. In contrast, participants scoring high in AI Literacy (AIL) demonstrated a deeper understanding of AI interaction, which influenced their prompting strategies; they tended to adhere closely to the exact wording of the brief, treating it as a set of precise specifications. The study’s findings have the potential to significantly impact education, both by encouraging students to experiment with prompt variations and deepen their expressive visual vocabulary, and by informing the design of curricula that incorporate structured exercises in iterative prompt refinement, explicitly teaching how to adjust prompts based on AI output to achieve desired visual outcomes.