GRAPHIC--Guidelines for Reviewing Algorithmic Practices in Human-centred Design and Interaction for Creativity

• URL: http://arxiv.org/abs/2511.17443v2
• Date: Mon, 24 Nov 2025 09:38:31 GMT
• Title: GRAPHIC--Guidelines for Reviewing Algorithmic Practices in Human-centred Design and Interaction for Creativity
• Abstract summary: GRAPHIC (Guidelines for Reviewing Algorithmic Practices in Human-centred Design and Interaction for Creativity) is a framework for analysing computational systems applied to Graphic Design.<n>Its goal is to understand how current systems support human-AI collaboration in the Graphic Design discipline.
• Score: 0.29790323103591465
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artificial Intelligence (AI) has been increasingly applied to creative domains, leading to the development of systems that collaborate with humans in design processes. In Graphic Design, integrating computational systems into co-creative workflows presents specific challenges, as it requires balancing scientific rigour with the subjective and visual nature of design practice. Following the PRISMA methodology, we identified 872 articles, resulting in a final corpus of 71 publications describing 68 unique systems. Based on this review, we introduce GRAPHIC (Guidelines for Reviewing Algorithmic Practices in Human-centred Design and Interaction for Creativity), a framework for analysing computational systems applied to Graphic Design. Its goal is to understand how current systems support human-AI collaboration in the Graphic Design discipline. The framework comprises main dimensions, which our analysis revealed to be essential across diverse system types: (1) Collaborative Panorama, (2) Processes and Modalities, and (3) Graphic Design Principles. Its application revealed research gaps, including the need to balance initiative and control between agents, improve communication through explainable interaction models, and promote systems that support transformational creativity grounded in core design principles.

Related papers

• GenAI for Systems: Recurring Challenges and Design Principles from Software to Silicon [62.2138479061386]
  Generative AI is reshaping how computing systems are designed, optimized, and built, yet research remains fragmented across software, architecture, and chip design communities.<n>This paper takes a cross-stack perspective, examining how generative models are being applied from code generation and distributed runtimes through hardware design space exploration to RTL synthesis, physical layout, and verification.
  arXiv  Detail & Related papers  (2026-02-16T22:45:33Z)
• Human-AI Co-Creation: A Framework for Collaborative Design in Intelligent Systems [0.6526824510982802]
  This paper explores the emergent paradigm of human-AI co-creation, where AI is not merely used for automation or efficiency gains, but actively participates in ideation, visual conceptualization, and decision-making.<n>Specifically, we investigate the use of large language models (LLMs) like GPT-4 and multimodal diffusion models such as Stable Diffusion as creative agents that engage designers in iterative cycles of proposal, critique, and revision.
  arXiv  Detail & Related papers  (2025-07-22T04:29:33Z)
• Creativity in LLM-based Multi-Agent Systems: A Survey [56.25583236738877]
  Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts.<n>This is the first survey dedicated to creativity in MAS.<n>We focus on text and image generation tasks, and present: (1) a taxonomy of agent proactivity and persona design; (2) an overview of generation techniques, including divergent exploration, iterative refinement, and collaborative synthesis, as well as relevant datasets and evaluation metrics; and (3) a discussion of key challenges, such as inconsistent evaluation standards, insufficient bias mitigation, coordination conflicts, and the lack of unified benchmarks.
  arXiv  Detail & Related papers  (2025-05-27T12:36:14Z)
• Towards a Working Definition of Designing Generative User Interfaces [1.1603243575080535]
  Generative UI is transforming interface design by facilitating AI-driven collaborative between designers and computational systems.<n>This study establishes a working definition of Generative UI through a multi-method qualitative approach.
  arXiv  Detail & Related papers  (2025-05-21T03:14:09Z)
• SYNTHIA: Novel Concept Design with Affordance Composition [114.19366716161655]
  We introduce SYNTHIA, a framework for generating novel, functionally coherent designs based on desired affordances.<n>We develop a curriculum learning scheme based on our ontology that contrast fine-tunes T2I models to progressively learn affordance composition.<n> Experimental results show that SYNTHIA outperforms state-of-the-art T2I models.
  arXiv  Detail & Related papers  (2025-02-25T02:54:11Z)
• Diffusion-Based Visual Art Creation: A Survey and New Perspectives [51.522935314070416]
  This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation. We aim to shed light on the mechanisms through which AI systems emulate and possibly, enhance human capacities in artistic perception and creativity.
  arXiv  Detail & Related papers  (2024-08-22T04:49:50Z)
• MetaDesigner: Advancing Artistic Typography Through AI-Driven, User-Centric, and Multilingual WordArt Synthesis [65.78359025027457]
  MetaDesigner introduces a transformative framework for artistic typography, powered by Large Language Models (LLMs)<n>Its foundation is a multi-agent system comprising the Pipeline, Glyph, and Texture agents, which collectively orchestrate the creation of customizable WordArt.
  arXiv  Detail & Related papers  (2024-06-28T11:58:26Z)
• Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams [2.237039275844699]
  Generative AI models are increasingly being integrated into human task, enabling the production of expressive content.<n>Unlike traditional human-AI design methods, the new approach to designing generative capabilities focuses heavily on prompt engineering strategies.<n>Our findings highlight emerging practices and role shifts in AI system prototyping among multistakeholder teams.
  arXiv  Detail & Related papers  (2024-02-27T17:56:10Z)
• From Metrics to Meaning: Time to Rethink Evaluation in Human-AI Collaborative Design [0.0]
  This paper contributes a challenge to rethink how to evaluate human--AI collaborative systems.<n>We present findings from one of the largest field studies to date of a human--AI co-creative system.<n>We propose evaluating human--AI systems holistically and considering intelligent systems as a core part of the user experience.
  arXiv  Detail & Related papers  (2024-01-30T08:54:46Z)
• Human Machine Co-Creation. A Complementary Cognitive Approach to Creative Character Design Process Using GANs [0.0]
  Two neural networks compete to generate new visual contents indistinguishable from the original dataset. The proposed approach aims to inform the process of perceiving, knowing, and making. The machine generated concepts are used as a launching platform for character designers to conceptualize new characters.
  arXiv  Detail & Related papers  (2023-11-23T12:18:39Z)
• Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques [55.492211642128446]
  Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. Model-based computational design algorithms assist designers by generating design examples during design. Black box methods for assistance, on the other hand, can work with any design problem.
  arXiv  Detail & Related papers  (2022-04-15T20:40:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.