## 6 Conclusions and Future Research  
This study introduced a conceptual framework that integrates live-prototyping with generative artificial intelligence (AI) to explore new possibilities for enhancing the user-centered design (UCD) process. By enabling designers to modify prototypes in real time through AI prompting and chaining, the framework aims to facilitate more immediate user feedback without the need for high-fidelity component development. Traditional UCD workflows often require multiple iterative cycles, which can be time-consuming and resource-intensive. Live-prototyping may offer a way to address these challenges by enabling instant adjustments to facilitate early evaluation of design elements and foster a more dynamic and iterative design process. While conceptually inspired by the Wizard-of-Oz (WoZ) technique, our framework distinguishes itself by leveraging AI-generated components to support more flexible, adaptive prototyping. To investigate the viability of this concept, we employed an exploratory qualitative study. We conducted a case study with nine experienced prototypes who engaged in a simulated live-prototyping session based on real feedback gathered in a preliminary user study. Semi-structured interviews were then used to explore their experiences, attitudes, and interpretations of the framework. A thematic analysis of these interviews revealed both promising opportunities and important challenges. Findings suggest that the framework may support early-stage ideation and improve communication between designers and stakeholders. Participants appreciated the potential for quicker feedback loops, increased transparency, and fast component simulation using generative AI. However, they also highlighted several challenges, such as the steep learning curve associated with prompt design and AI chaining, as well as concerns around the reliability, interpretability, and consistency of AI-generated outputs. It is important to emphasize that our study does not provide empirical evidence that AI-assisted live-prototyping is faster, more effective, or more insightful than traditional prototyping methods. These claims were discussed as perceived potential based on qualitative feedback, not as statistically validated outcomes. Due to the small sample size and the qualitative nature of the study, no generalizations should be drawn regarding performance improvements, iteration speed, or usability benefits. Future work should include quantitative studies with larger and more diverse participant samples to evaluate the practical impact of AI-assisted live-prototyping. This could include comparative study designs focusing on the number of iterations needed, time required for revisions, and usability metrics or cognitive load assessments between different workflows. Quantitative data collection would help measure user perceptions of effectiveness, usability, and collaboration. These efforts are essential for substantiating the framework's benefits with robust empirical evidence. Despite these challenges, participants expressed strong interest in a dedicated AI live-prototyping toolbox that would provide intuitive access to a wide range of AI tools for prompting and chaining. Such a system could significantly lower the barrier to entry for novice users, allowing them to leverage existing and emerging AI technologies to enhance their prototyping processes. Furthermore, as generative AI continues to evolve, future advancements may enable more sophisticated component simulations, expanding the possibilities of AI-assisted prototyping. Investigating the development of standardized toolkits and frameworks tailored for live-prototyping could further refine this approach and improve its accessibility for designers with varying levels of technical expertise. Future investigations could focus on refining AI chaining strategies, improving the interpretability of AI-generated outputs, and mitigating potential biases introduced by both designers and AI models.