Effects of UI Abstraction on Non‑Expert Prompting Workflows in Generative AI

Generative AI tools offer powerful capabilities but often demand that users craft precise prompts, effectively requiring humans to “think like machines.” This thesis investigates whether thoughtful interface design can instead enable generative AI to better speak like us, aligning with natural human communication. We conducted an exploratory study with 10 non-expert participants, observing each as they interacted with three Figma-based prototype interfaces of varying abstraction (ranging from a freeform text input to a highly guided prompt form) to complete a prompt-writing task. Our findings show that the level of UI abstraction significantly shaped users’ prompting experience. Participants preferred different interfaces depending on their familiarity and comfort: a freeform prompt felt most intuitive and expressive for some, whereas others benefitted from structured templates that yielded clearer prompts and greater confidence. These results highlight that a one-size-fits-all prompting solution is suboptimal. We discuss implications for the UX design of AI-powered tools, emphasizing the need for adaptive, user-centered prompt interfaces that accommodate varying experience levels and facilitate more natural human–AI interaction.