## 3 Conceptual Framework and Methods  
While the UCD process includes users for better design and evaluation, it often leads to multiple user study sessions, which require appropriate amounts of time and effort. This process can be seen as time-consuming and costly, especially when turning user feedback into components. To address this, we propose the idea of live-prototyping, which allows designers to make changes to the prototype immediately to evaluate components before investing time and resources in a full implementation. To facilitate live-prototyping, we propose a conceptual framework, as seen in Figure 1.  
To make live changes to the prototype, designers need to be able to modify it during user testing. To achieve this, our conceptual framework requires that the necessary elements of the prototype be built with generative AI, allowing components to be dynamically created and modified. These AI-driven components can be manipulated using different prompts, changing their behavior and interaction. The prompting process varies based on the generative AI being used: in the case of ChatGPT, such instructions utilize clear text [47], while Stable Diffusion requires more complex prompting through a dashboard. Our conceptual framework employs one or more generative AI models to simulate components of the prototype. These components represent features that users interact with during testing. If participants request any changes to the prototype, the designer can immediately validate these changes by restructuring simulated components to better align with the participants' needs. For this, our conceptual framework proposes that prototypes utilized in user studies should comprise two interfaces: one directed toward the participant and a control interface for the prototype designer, which is not visible to the participant. This control interface allows designers to adjust how AI models generate components through prompts and how components interact with each other. The concept of simulating components of a prototype is similar to the Wizard-of-Oz (WOZ) simulation technique. However, the reliance on a human \"wizard\" to simulate digital interactions presents limitations, particularly in tasks where digital prototyping demands more sophisticated solutions. Generative AI emerges as a viable alternative in this context. To facilitate the generation of more complex components, our framework encourages the use of multiple AI models, where the output of one model becomes the input of another. This method of linking multiple AI models is akin to having a collection of Wizards of Oz at the designer's disposal. This approach could allow for the immediate  
Figure 1: Conceptual framework for live-prototyping with generative AI: participants interact with a GUI-based prototype during study sessions and provide feedback, while designers use a control interface to adjust AI-driven components that simulate new or modified interface elements. and seamless integration of user feedback into the prototype, potentially reducing the need for creating high-fidelity components during the prototyping phase. To demonstrate our conceptual framework, we built a prototype according to the principles outlined in Figure 1. We then invited HCI practitioners with prototyping skills to participate in a study where they could actively engage in live-prototyping and reflect on the framework's underlying ideas. In our study, they were tasked with leading a co-design session with an end-user, where they would redesign and adjust our prototype in real time based on the user's feedback. To establish a controlled environment focusing on the live prototype, one of our researchers played the role of the end-user. This end-user voiced feedback during the co-design session, representing realistic concerns that the HCI practitioners were asked to address through real-time changes.