What About My Design Context?: Exploring the Use of Generative AI to Support Customization of Translational Research Artifacts

Despite the wealth of knowledge in research papers, practitioners struggle to apply research results to their work due to significant research-practice gaps. This study addresses the rigor-relevance paradox, where academic rigor can undermine the practical relevance of research for designers. Specifically, we explore the potential of large language models (LLMs) to customize translational research artifacts (i.e., design cards) and improve relevance to specific designers' needs. In our preliminary study (N=15), designers defined relevance as alignment between the content of the translational artifact and their design context—including target users, modalities/domains, and design stages. Based on these findings, we implemented an LLM-powered pipeline that allows designers to customize research papers into design cards tailored to their contexts. Our evaluation (N=20) demonstrated that designers perceived customized artifacts as more relevant, actionable, valid, generative, and inspiring than those without customization—even for less topically related papers—indicating LLM-powered customization can be used to support research translation.