Demystifying Tacit Knowledge in Graphic Design: Characteristics, Instances, Approaches, and Guidelines
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Abstract (arXiv)
Despite the growing demand for professional graphic design knowledge, the tacit nature of design inhibits knowledge sharing. However, there is a limited understanding on the characteristics and instances of tacit knowledge in graphic design. In this work, we build a comprehensive set of tacit knowledge characteristics through a literature review. Through interviews with 10 professional graphic designers, we collected 123 tacit knowledge instances and labeled their characteristics. By qualitatively coding the instances, we identified the prominent elements, actions, and purposes of tacit knowledge. To identify which instances have been addressed the least, we conducted a systematic literature review of prior system support to graphic design. By understanding the reasons for the lack of support on these instances based on their characteristics, we propose design guidelines for capturing and applying tacit knowledge in design tools. This work takes a step towards understanding tacit knowledge, and how this knowledge can be communicated.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Mar 2024.
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Citing papers per year: 2025: 6; 2026: 4.
10 papers.
- 2025How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupationscited by 19
- 2025GraphicBench: A Planning Benchmark for Graphic Design with Language Agentscited by 4
- 2025ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inferencecited by 4
- 2025Improving User Interface Generation Models from Designer Feedbackcited by 3
- 2025CANVAS: A Benchmark for Vision-Language Models on Tool-Based User Interface Designcited by 3
- 2025Agentic Design Review Systemcited by 2
- 2026From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineeringcited by 1
- 2026Design Generative AI for Practitioners: Exploring Interaction Approaches Aligned with Creative Practicecited by 0
- 2026TUX: Measuring Human--AI Tacit Understandingcited by 0
- 2026Through the PRISM: Principle-Aware, Interpretable, and Multi-Scale Evaluation of Visual Designscited by 0
- 2026Through the PRISM: Principle-Aware, Interpretable, and Multi-Scale Evaluation of Visual Designs30 May 2026
- 2026TUX: Measuring Human--AI Tacit Understanding29 May 2026
- 2026Design Generative AI for Practitioners: Exploring Interaction Approaches Aligned with Creative Practice3 Mar 2026
- 2026From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineering31 Jan 2026
- 2025CANVAS: A Benchmark for Vision-Language Models on Tool-Based User Interface Design25 Nov 2025
- 2025How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupations26 Oct 2025
- 2025Improving User Interface Generation Models from Designer Feedback20 Sep 2025
- 2025ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference18 Sep 2025
- 2025Agentic Design Review System14 Aug 2025
- 2025GraphicBench: A Planning Benchmark for Graphic Design with Language Agents15 Apr 2025