A new generation of GenAI tools fueled by vibe coding practices promises to democratize software development, explicitly targeting users without programming backgrounds. Yet, we lack understanding of how technical and non-technical users actually engage with these tools across the product development lifecycle. We conducted a mixed-methods study combining an online survey (N = 85) with interviews of hackathon participants (N = 31) and practitioners (N = 8), examining how different user groups employ chatbots, local development assistants, and cloud development environments from ideation through deployment. Our findings reveal that cloud development environments accelerate prototyping, enabling non-technical users to generate high-fidelity "throw-away" prototypes valuable for experiential exploration. However, deployment and long-term maintainability remain dependent on technical expertise, with non-technical users consistently encountering barriers when transitioning beyond prototyping. We contribute a comparative analysis of how technical and non-technical users appropriate GenAI tools across the full product development cycle in contexts approximating real-world product building, highlighting implications for tool design and educational practices.

In what ways do generative AI (GenAI) tools embedded in mainstream design software affect UI design practices and outcomes? In this study, we examine the use of FigmaAI, a GenAI feature within the industry-standard platform Figma. We conducted a within-subject study with 16 professional UX designers, each of whom completed two high-fidelity UI design tasks in a think-aloud session: one conventionally, without AI, and one with FigmaAI. We analyse the design process itself alongside experiences and reflections on using and not using AI, gathered from adjacent semi-structured interviews and post-task questionnaires. Our findings suggest that GenAI reshapes UI workflows, shifting them from additive to subtractive processes: designers refine AI drafts rather than building interfaces from scratch. While AI reduces workload and accelerates initial setup, it also constrains exploration, limits perceived ownership, and produces designs that are more visually and structurally similar.

Large language models (LLMs) like ChatGPT have been widely adopted in work contexts. We explore the impact of ChatGPT on young professionals’ perception of productivity and sense of accomplishment. We collected LLMs’ main use cases in knowledge work through a preliminary study, which served as the basis for a two-week diary study with 21 young professionals reflecting on their ChatGPT use. Findings indicate that ChatGPT enhanced some participants’ perceptions of productivity and accomplishment by enabling greater creative output and satisfaction from efficient tool utilization. Others experienced decreased perceived productivity and accomplishment, driven by a diminished sense of ownership, perceived lack of challenge, and mediocre results. We found that the suitability of task delegation to ChatGPT varies strongly depending on the task nature. It’s especially suitable for comprehending broad subject domains, generating creative solutions, and uncovering new information. It’s less suitable for research tasks due to hallucinations, which necessitate extensive validation.

Engaging first-time users of mobile apps is challenging. Onboarding task flows are designed to minimize the drop out of users. To this point, there is little scientific insight into how to design these task flows. We explore this question with a specific focus on financial applications, which pose a particularly high hurdle and require significant trust. We address this question by combining two approaches. We first conducted semi-structured interviews (n=16) exploring users’ meaning-making when engaging with new mobile applications in general. We then prototyped and evaluated onboarding task flows (n=16) for two mobile cryptocurrency apps using the minimalist instruction framework. Our results suggest that well-designed onboarding processes can improve the perceived usability of first-time users for feature-rich mobile apps. We discuss how the expectations users voiced during the interview study can be met by applying instructional design principles and reason that the minimalist instruction framework for mobile onboarding insights presents itself as a useful design method for practitioners to develop onboarding processes and also to identify when not to.