Smart Color Palette Suggestions Based on Intent, Audience, and Design Context

Abstract

Color selection is a crucial aspect of web and app design that significantly influences user engagement, emotional impact, and usability. However, many developers and coders lack formal training in design, leading to suboptimal color choices. Existing color palette generators offer limited support as they rarely consider the three essential factors: intent, audience, and design context. This paper explores the current landscape of smart color recommendation systems and highlights this gap. We propose a conceptual framework that assists developers by asking targeted questions about their design goal, target audience (age group, industry), and content type. This framework will suggest appropriate color palettes aligned with psychological and cultural color theories. Our goal is to make design more accessible for developers and improve the overall quality of digital interfaces.

Introduction

Color plays a critical role in the visual appeal and user experience of websites and applications, affecting user perception, mood, and engagement. However, many developers—especially those without design expertise—struggle to select appropriate color schemes. Poor color choices can result in unprofessional or ineffective interfaces.

Problem & Proposal
Current color tools (e.g., Adobe Color, Colormind) help generate palettes based on color theory or design aesthetics but lack customization based on audience, context, or intent. To address this, the authors propose a machine learning-based system that suggests color palettes tailored to:

• Design intent (e.g., playful, formal)

• Target audience (e.g., children, professionals)

• Content context (e.g., website, app)

Methodology

1. Data Collection & Annotation: Build a dataset of UI screenshots labeled with metadata such as audience, intent, color palette, and performance metrics (e.g., engagement).

2. Developer Questionnaire Interface: Developers provide structured input about their project (e.g., tone, target users, industry).

3. Model Training: Use supervised machine learning (e.g., MLPs, decision trees, or transformers) to learn mappings between input features and successful color palettes.

4. Evaluation: Assess output through expert review, A/B testing, and UX metrics (e.g., bounce rate, engagement).

Expected Results & Applications
The system aims to deliver personalized, context-aware color suggestions that help non-designers create appealing, effective interfaces. It can be integrated into web builders, learning tools, or IDEs, evolving over time via user feedback and data-driven improvements.

Conclusion

Design plays an essential role in the usability and success of digital products. Developers, while skilled in coding, often lack design expertise, particularly in color selection. This paper identifies a critical gap in existing tools and proposes a simple but impactful solution: integrating intent, audience, and design context into the palette suggestion process. By doing so, we can bridge the gap between development and design, creating more effective and meaningful digital experiences. Future work includes building a rule-based prototype, testing it with real users, and eventually training a machine learning model for automated suggestions. This approach offers both practical value and research potential in the intersection of design, psychology, and human-computer interaction.

References

[1] Sherin, Aaris. Design elements, Color fundamentals: A graphic style manual for understanding how color affects design. Rockport Publishers, 2012. [2] Ou, L. C., & Luo, M. R. (2006). A colour harmony model for two-colour combinations. Color Research & Application. [3] Palmer, S. E., & Schloss, K. B. (2010). An ecological valence theory of human color preference. Proceedings of the National Academy of Sciences. [4] Tidwell, Jenifer. Designing interfaces: Patterns for effective interaction design. \" O\'Reilly Media, Inc.\", 2010. [5] Kim, Sooji. My Color: Finding Your Optimal Color Style through ML. University of Washington, 2020. [6] Hall, R. (2019). Color Psychology in UI Design. Smashing Magazine. [7] Leborg, C. (2006). Visual Grammar. Princeton Architectural Press. [8] Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Copyright

Copyright © 2025 Samruddhi Milind Save. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.