The paper "From Paper to Card: Transforming Design Implications with Generative AI" by Donghoon Shin, Lucy Lu Wang, and Gary Hsieh explores the use of generative AI models to create design cards from academic papers. The authors address the challenge of making design implications from academic papers more accessible and engaging for designers. They propose a system that uses large language models (LLMs) and text-to-image models to automatically generate design cards, which are designed to communicate valuable insights from papers in a more digestible and accessible format. The study includes a preliminary interview with 12 designers to understand their preferences for design cards and an iterative design process to refine the card components. The final design card includes a title, description, image, source text of the design implication, paper summary, evidence, and citation. The system is implemented as a web interface that allows users to upload a paper PDF and select the design implication text to generate a design card. The evaluation with 21 designers and 12 authors of selected papers revealed that designers perceived the content from AI-generated design cards as more inspiring and generative compared to reading the original paper texts. Authors also found the generated content clear and accurate, highlighting the potential of design cards in strengthening the delivery of their contributions in HCI. The paper concludes with insights for future enhancements of AI-generated design cards, emphasizing the importance of visual hierarchy, concise language, and the use of auto-generated images to support the communication of design implications.The paper "From Paper to Card: Transforming Design Implications with Generative AI" by Donghoon Shin, Lucy Lu Wang, and Gary Hsieh explores the use of generative AI models to create design cards from academic papers. The authors address the challenge of making design implications from academic papers more accessible and engaging for designers. They propose a system that uses large language models (LLMs) and text-to-image models to automatically generate design cards, which are designed to communicate valuable insights from papers in a more digestible and accessible format. The study includes a preliminary interview with 12 designers to understand their preferences for design cards and an iterative design process to refine the card components. The final design card includes a title, description, image, source text of the design implication, paper summary, evidence, and citation. The system is implemented as a web interface that allows users to upload a paper PDF and select the design implication text to generate a design card. The evaluation with 21 designers and 12 authors of selected papers revealed that designers perceived the content from AI-generated design cards as more inspiring and generative compared to reading the original paper texts. Authors also found the generated content clear and accurate, highlighting the potential of design cards in strengthening the delivery of their contributions in HCI. The paper concludes with insights for future enhancements of AI-generated design cards, emphasizing the importance of visual hierarchy, concise language, and the use of auto-generated images to support the communication of design implications.