From Paper to Card: Transforming Design Implications with Generative AI
<|reference_start|>From Paper to Card: Transforming Design Implications with Generative AI: Communicating design implications is common within the HCI community when publishing academic papers, yet these papers are rarely read and used by designers. One solution is to use design cards as a form of translational resource that communicates valuable insights from papers in a more digestible and accessible format to assist in design processes. However, creating design cards can be time-consuming, and authors may lack the resources/know-how to produce cards. Through an iterative design process, we built a system that helps create design cards from academic papers using an LLM and text-to-image model. Our evaluation with designers (N=21) and authors of selected papers (N=12) revealed that designers perceived the design implications from our design cards as more inspiring and generative, compared to reading original paper texts, and the authors viewed our system as an effective way of communicating their design implications. We also propose future enhancements for AI-generated design cards.<|reference_end|>
@article{shin2024from,
    title={From Paper to Card: Transforming Design Implications with Generative AI},
    author={Donghoon Shin, Lucy Lu Wang, Gary Hsieh},
    journal={In Proceedings of the CHI Conference on Human Factors in Computing
  Systems (CHI '24), May 11-16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA},
    year={2024},
    doi={10.1145/3613904.3642266},
    archivePrefix={arXiv},
    eprint={2403.08137},
    primaryClass={cs.HC cs.AI cs.CL}
}