## 5 Discussion  
### General Perceptions During Framework Application  
Traditional user-centered design (UCD) methodologies often struggle to capture the immediacy and depth of user feedback, necessitating multiple iterations before arriving at a refined solution. By integrating generative AI into live-prototyping, our conceptual framework could enable designers to address explicit user requests in real time and uncover underlying motivations, referred to as meta-feedback. This deeper insight is particularly valuable for understanding user needs beyond surface-level preferences. Several participants observed that live-prototyping tends to foster more personalized interactions and may streamline the design process by reducing the number of required iterations, suggesting that real-time adaptability broadens the design space and elicits more nuanced feedback than traditional static approaches. These findings align with prior research emphasizing the importance of capturing user context and latent needs in AI-supported UCD processes [14] and highlight the role of interactive systems in aligning with users' mental models to reveal deeper design requirements [24]. One of the main advantages of our framework is its potential to reduce the number of iterations in prototyping studies. In a previous study, Subramonyam et al. demonstrated that balancing iterative flexibility with technical precision is crucial in AI-integrated design workflows, while Siricharoien suggests that incorporating live feedback could enhance prototyping efficiency [14,49]. Similarly, some participants indicated that the framework could improve efficiency by offering instant feedback and facilitating rapid adjustments that might lead to more valuable insights. However, while this efficiency may accelerate the design process, it could also introduce new demands. The semi-structured nature of live-prototyping requires rigorous planning and a high level of technical expertise in AI prompting and chaining. Other participants indicated that preparing for live-prototyping studies might need more time than traditional approaches, implying that investing more in initial planning and creative problem-solving during execution could be necessary. Beyond its impact on iteration cycles, the framework demonstrates utility in early-stage design exploration. The ability to generate and refine ideas in real time fosters an iterative, dynamic brainstorming environment that supports rapid ideation, as several studies have observed [23]. Participants generally noted that live-prototyping offers a more insightful and exploratory design process than static approaches, a sentiment also echoed by Bilgram and Laarmann [23]. This indicates that by facilitating immediate feedback loops, the framework can strengthen the dialogue between user insights and design refinement, which could lead to richer conceptualization. Moreover, Ray has highlighted that such real-time methods accelerate early-stage innovation by reducing the gap between conceptual ideas and prototype realization [13]. Another critical benefit of the framework is its potential role in improving stakeholder communication. Some participants suggested that live-prototyping could foster a more effective exchange between designers, developers, and collaborators by potentially addressing misunderstandings early in the design process. A few participants indicated that live-prototyping might serve as a valuable communication tool that could facilitate early resolution of misunderstandings and promote smoother transitions to development. Furthermore, the visibility of real-time design iterations might allow stakeholders to engage in a more transparent and iterative decision-making process, which could help reduce misalignment and promote a shared understanding of design goals. For instance, Subramonyam et al. have highlighted how collaborative prototyping environments can enhance cross-functional teamwork [49]. Evidence also suggests that dynamic feedback mechanisms may support more coherent stakeholder alignment [24].