1. What problem are you trying to solve, and why should people care?
AI is rapidly becoming part of UX designers’ everyday work. It can help generate design concepts, speed up repetitive tasks, and spot patterns in large-scale data. That sounds promising, but also unsettling: as AI takes on more tasks, how do designers avoid over-relying on it, and how might it influence designers’ creativity, ownership, and professional development? These questions become even more urgent in high-stakes contexts such as truck interaction design, where design decisions can influence safety-critical systems and long-term sustainability goals. In such contexts, designers cannot simply trust AI outputs at face value. They need support that is useful, but also transparent, grounded in real-world practice, and open to scrutiny.
2. What are you actually doing to tackle it?
My project explores how AI tools can be designed to support meaningful collaboration between designers and AI. I adopt a human-centered approach and work closely with professional designers in truck interaction design to understand what they need from AI, what they expect from it, and how they actually engage with it. To explore this, I carried out interviews, observations, and engaged designers in co-designing and evaluating an AI-supported prototype. Across these studies, AI-supported data sensemaking became the central focus, informing high-stakes design decisions with user scenarios derived from real-world data.
3. What difference could this make?
Right now, we are still quite far from the kind of designer–AI collaboration we often imagine. Human collaboration involves communication, shared goals, negotiation, and mutual support, while AI is still limited in its ability to participate in that kind of relationship. This is why the design of AI tools matters so much: AI can be powerful, but it can also be harmful if it is introduced in ways that reduce human judgment, hide uncertainty, or encourage over-reliance. In the short term, my work shows how AI can help designers explore user scenarios in large-scale data and identify where deeper qualitative research is needed. In the longer term, it contributes to a better understanding of the elements that shape the dynamics of designer–AI collaboration and of what must be designed thoughtfully to support responsible use in high-stakes contexts.
