The dissemination of scholarly research is critical, yet researchers often lack the time and skills to create engaging content for popular media such as short-form videos. To address this gap, we explore the use of generative AI to help researchers transform their academic papers into accessible video content. Informed by a formative study with science communicators and content creators (N = 8), we designed PaperTok, an end-to-end system that automates the initial creative labor by generating script options and corresponding audiovisual content from a source paper. Researchers can then refine based on their preferences with further prompting. A mixed-methods user study (N = 18) and crowdsourced evaluation (N = 100) demonstrate that PaperTok’s workflow can help researchers create engaging and informative short-form videos. We also identified the need for more fine-grained controls in the creation process. To this end, we offer implications for future generative tools that support science outreach.

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.

The popularity of task-oriented chatbots is constantly growing, but smooth conversational progress with them remains profoundly challenging. In recent years, researchers have argued that chatbot systems should include guidance for users on how to converse with them. Nevertheless, empirical evidence about what to place in such guidance, and when to deliver it, has been lacking. Using a mixed-methods approach that integrates results from a between-subjects experiment and a reflection session, this paper compares the effectiveness of eight combinations of two guidance types (example-based and rule-based) at four guidance timings (service-onboarding, task-intro, after-failure, and upon-request), as measured by users’ task performance, improvement on subsequent tasks, and subjective experience. It establishes that each guidance type and timing has particular strengths and weaknesses, thus that each type/timing combination has a unique impact on performance metrics, learning outcomes, and user experience. On that basis, it presents guidance-design recommendations for future task-oriented chatbots.