ARTURE: An AI-Driven Design Platform for Structured, Editable Layouts and Culturally Responsive Visual Communication | IJET – Volume 12 Issue 2 | IJET-V12I2P52

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International Journal of Engineering and Techniques (IJET)

Open Access • Peer Reviewed • High Citation & Impact Factor • ISSN: 2395-1303

Volume 12, Issue 2  |  Published: April 2026

Author: Jitesh S. Bangera, Nilay P. Kavire, Khushi V. Pathak, Dr. Sachin More

DOI: https://doi.org/{{doi}}  â€˘  PDF: Download

Abstract

Graphic design is a pillar of visual communication, which has traditionally been reliant on a complex mix of technical expertise, creative skill and human instinct. The rapid evolution of Artificial Intelligence (AI) and generative models has opened avenues to transform the design industry, moving it towards a collaborative approach where humans and AI work in tandem [1], [2]. Many current platforms rely on static template-based structures that severely limit creativity, or generate flattened images that prevent granular editing [5], [6]. Moreover, many existing algorithms lack cultural and visual semantic backing, leading to overly generalized outputs that do not cater to regional preferences [7], [8]. To address these challenges, we present ARTURE, an AI-enhanced web-based design platform that bridges the gap between rudimentary template-based tools and professional design software. The system combines Natural Language Processing with vector-based semantic search to retrieve culturally adaptable templates mapped to the user’s prompt. The proposed architecture outputs structured, multi-layer design data rendered on an editable web canvas, enabling a fully-editable interface for text, uploaded media, and template layout [9], [10]. A human-in-the- loop approach preserves complete designer control while enhancing productivity [11], [12]. Results highlight the potential of AI as a creative companion that democratizes professional-grade graphic design.

Keywords

Artificial Intelligence, Design Automation, Generative AI, Web-Based Design Platform, Canvas Editing, Human-AI Collaboration.

Conclusion

ARTURE demonstrates that structured, editable, culturally-aware AI-generated design is achievable within a single web-based platform. By combining NLP-driven prompt interpretation, vector-based semantic template retrieval, hybrid AI inference, and Fabric.js canvas rendering, the system bridges the long-standing gap between accessible template tools and professional-grade design software. The implementation confirms that AI is most effective not as a replacement for human creativity but as a collaborative partner that handles structural composition while the designer retains full authority over every detail. The platform’s modular architecture positions it well for future expansion into 3D design, real-time collaboration, and richer cultural representation, pointing toward a more inclusive and democratized design ecosystem.

References

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Cite this article

APA
Jitesh S. Bangera, Nilay P. Kavire, Khushi V. Pathak, Dr. Sachin More (April 2026). ARTURE: An AI-Driven Design Platform for Structured, Editable Layouts and Culturally Responsive Visual Communication. International Journal of Engineering and Techniques (IJET), 12(2). https://doi.org/{{doi}}
Jitesh S. Bangera, Nilay P. Kavire, Khushi V. Pathak, Dr. Sachin More, “ARTURE: An AI-Driven Design Platform for Structured, Editable Layouts and Culturally Responsive Visual Communication,” International Journal of Engineering and Techniques (IJET), vol. 12, no. 2, April 2026, doi: {{doi}}.
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