AI 能否节省产品设计时间？AI 提示到设计工作流的随机对照实验

Computer Science > Human-Computer Interaction arXiv:2609.26725 (cs) [Submitted on 22 Sep 2026] Title:Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows Authors:Remy Stewart, Olabode Anise, Andrew Hogan, Augustus Griffin View a PDF of the paper titled Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows, by Remy Stewart and 3 other authors View PDF HTML (experimental) Abstract:AI tools for digital product design now offer prompt-to-design capabilities, allowing designers and their non-designer colleagues to create prototypes through conversational workflows with large language models (LLMs). While these tools promise time savings, experimental evidence in product design remains limited compared with evidence from software engineering. We conducted a randomized controlled trial with 50 product designers and 50 product managers to evaluate prospective time savings from leveraging Figma Make in design work. Participants attempted three standardized design tasks with or without access to Figma Make. Among participants who completed the study tasks, access to Figma Make was associated with approximately 20% shorter completion times, with larger gains among product managers. Our findings suggest that prompt-to-design tools may enable product managers to further contribute to design work, while the benefits for professional designers may be task dependent. Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.26725 [cs.HC] (or arXiv:2609.26725v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2609.26725 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Remy Stewart [view email] [v1] Tue, 22 Sep 2026 17:13:07 UTC (1,190 KB) Full-text links: Access Paper: View a PDF of the paper titled Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows, by Remy Stewart and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.HC new | recent | 2026-09 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)