AI Prototyper : A Figma Plugin for Decomposition-Based GUI Prototyping with LLMs
Graphical user interface (GUI) prototyping remains a time-consuming activity that demands both design expertise and considerable manual effort. As GUI prototypes are non-code artifacts that evolve alongside requirements throughout the development cycle, automating their generation is directly relevant to software maintenance and evolution. We present AI Prototyper, an open-source Figma plugin that automates GUI prototyping through a decomposition and retrieval-augmented generation (RAG) pipeline. Given a natural-language description of a desired screen, such as a login page or a product detail card, the plugin decomposes the request into discrete GUI features, retrieves matching components from a custom 32-primitive library, and renders each component as a fully editable Figma layer with auto-layout. The pipeline uses Gemini 2.5 Flash as its LLM back-end and a Node.js/Express service. Unlike existing decomposition-based tools, AI Prototyper introduces a human-in-the-loop editing step that lets users review, modify, or extend the generated feature list before rendering, uses a different technology stack and LLM family, and supports multilingual input, producing correctly labelled interfaces in Thai, English, and Mandarin Chinese. In a preliminary evaluation, participants using AI Prototyper completed more prototypes in a fixed time window than those working manually, and expert practitioners rated the AI-generated prototypes higher across nine quality dimensions. A demonstration video is available at https://youtu.be/pRoFAH7MQaE. The source code and component library are available at https://github.com/tongsalangsingha/AI-prototyper-tool.
Fri 18 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:00 - 17:30 | Session 27 - Tomorrow’s AI ToolboxTool Demonstration and Data Showcase Track / Visions and Emerging Results Track / Registered Reports / Research Papers Track at Auditorium Chair(s): Emanuela Guglielmi University of Molise Theme: Emerging Results, Tools & Industry Innovation | ||
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16:10 10mPaper | Training Dynamics of Neural Software Defect Predictors under Coupled Data-Quality Issues Registered Reports Emmanuel Charleson Dapaah University of Göttingen, Philip Makedonski Institute of Computer Science, University of Göttingen, Lower Saxony, Germany, Jens Grabowski Pre-print | ||
16:20 20mPaper | Smelly Bad Practices in Performance System Tests: Detection, Prevalence, and Lifetime Research Papers Track Sergio Di Meglio Università degli Studi di Napoli Federico II, Valeria Pontillo Gran Sasso Science Institute (GSSI), L’Aquila, Italy, Luigi Libero Lucio Starace Department of Electrical Engineering and Information Technology Università degli Studi di Napoli Federico II, Luana Martins University of Salerno, Dario Di Nucci Software Engineering (SeSa) Lab, University of Salerno, Italy, Fabio Palomba University of Salerno Pre-print | ||
16:40 20mPaper | SWIRL: Interactive Sensemaking of Tool-Generated Warnings through Customized Summaries Research Papers Track Link to publication Pre-print Media Attached | ||
17:00 10mShort-paper | AI Prototyper : A Figma Plugin for Decomposition-Based GUI Prototyping with LLMs Tool Demonstration and Data Showcase Track Tawatchai Salangsingha Edinburgh Napier University, Ashkan Sami Edinburgh Napier University, Md Zia Ullah Edinburgh Napier University, Iain McGregor Edinburgh Napier University Pre-print Media Attached | ||
17:10 10mShort-paper | Multi-Agent Repair of Dependency Updates in Kotlin Multiplatform Visions and Emerging Results Track Santiago Bobadilla-Suarez Universidad de los Andes, Colombia, Camilo Escobar-Velásquez Universidad de los Andes, Colombia | ||