## 4 Results
Additionally, participants appreciated the role of designers as intermediaries between users and AI systems, which allowed them to tailor simulations to specific prototyping goals. One participant remarked, \"In terms of the prototyper being a mediator between the user and AI, it is useful\". However, concerns emerged regarding the potential for inconsistencies in AI-generated results across different study participants, which could complicate the comparability of user tests. These findings indicate that while AI-driven simulations could potentially offer notable cost and efficiency benefits, ensuring consistency in AI outputs would remain a key challenge. Despite its advantages, live-prototyping introduces additional complexities in study planning and execution. One participant acknowledged this challenge, stating, \"Preparing for live-prototyping studies might take longer, but the flexibility during testing means less time invested in preparation.\" This suggests a trade-off between the initial time investment required for study setup and the adaptability gained during the testing phase. Another participant reinforced this point, explaining, \"Since prototyping is more personalized, you would need a lot of time planning and developing.\" All participants noted that live-prototyping requires increased spontaneity and creative problem-solving from designers. One participant mentioned, \"Conducting studies will become more complex, requiring spontaneity and creative skills from the designer.\" This complexity extends to data analysis, with some participants raising concerns about the need for alternative data structuring methods. Additionally, participants speculated that the real-time implementation of certain components might create slowdowns in study sessions, potentially testing users' patience. To mitigate such disruptions, some participants suggested that a deeper understanding of AI chaining and prompting could improve study fluidity. Finally, participants raised concerns regarding potential biases in AI-generated outputs and the overall research process. One participant warned, \"You may be too influenced by the singular opinion of a user, which could cause tunnel vision\", indicating that an overemphasis on individual feedback may inadvertently narrow the scope of inquiry. Another participant cautioned that \"Live-prototyping can lead to bias from the prototyper as to how they want certain features\", highlighting the risk of designer preferences shaping research outcomes. Beyond individual biases, participants also identified potential methodological concerns. One participant observed, \"The ease of addressing issues with AI tools might introduce bias into the research process\", suggesting that the convenience of AI-assisted modifications could unintentionally skew study findings. These concerns underline the importance of implementing robust methodological safeguards to mitigate bias and ensure the integrity of AI-assisted live-prototyping studies. The findings of this study reveal both the strengths and challenges of integrating generative AI into live-prototyping user studies. Participants highlighted the framework's ability to generate meaningful insights efficiently, facilitate clearer communication among stakeholders, and provide a cost-effective alternative to fully implemented prototypes. However, they also identified important challenges, including the increased complexity of planning live-prototyping studies and the potential for biases in both AI-generated outputs and the research process. While the framework enhances adaptability and real time engagement, its effectiveness depends on careful methodological considerations to balance user input, maintain consistency in AI behavior, and mitigate unintended influences on design decisions. Addressing these factors will be crucial for refining AI-assisted prototyping approaches and ensuring their reliability in future studies.