# Understanding Nonlinear Collaboration between Human and AI Agents: A Co-design Framework for Creative Design

arXiv: https://arxiv.org/abs/2401.07312 · HTML: https://mlsanity.com/paper/2401.07312/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2024.

## Built on by (most cited first, 22 of 22)

- [DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design](https://mlsanity.com/paper/2502.09867/) (2025)
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- [From Fake Perfects to Conversational Imperfects: Exploring Image-Generative AI as a Boundary Object for Participatory Design of Public Spaces](https://mlsanity.com/paper/2411.00949/) (2024)
- [Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-Design](https://mlsanity.com/paper/2502.09055/) (2025)
- [Carbon and Silicon, Coexist or Compete? A Survey on Human-AI Interactions in Agent-based Modeling and Simulation](https://mlsanity.com/paper/2502.18145/) (2025)
- [Flow State: Humans Enabling AI Systems to Program Themselves](https://mlsanity.com/paper/2504.03771/) (2025)
- [Exploring Collaborative GenAI Agents in Synchronous Group Settings: Eliciting Team Perceptions and Design Considerations for the Future of Work](https://mlsanity.com/paper/2504.14779/) (2025)
- [ACAI for SBOs: AI Co-creation for Advertising and Inspiration for Small Business Owners](https://mlsanity.com/paper/2503.06729/) (2025)
- [Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models](https://mlsanity.com/paper/2512.18388/) (2025)
- [Vidmento: Creating Video Stories Through Context-Aware Expansion With Generative Video](https://mlsanity.com/paper/2601.22013/) (2026)
- [LLM4DESIGN: An Automated Multi-Modal System for Architectural and Environmental Design](https://mlsanity.com/paper/2407.12025/) (2024)
- [Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective](https://mlsanity.com/paper/2503.02631/) (2025)
- [Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspective](https://mlsanity.com/paper/2504.10918/) (2025)
- [DashChat: Interactive Authoring of Performance Dashboard Design Prototypes through Conversation with LLM-Powered Agent](https://mlsanity.com/paper/2504.12865/) (2025)
- [From Description to Detection: LLM based Extendable O-RAN Compliant Blind DoS Detection in 5G and Beyond](https://mlsanity.com/paper/2510.06530/) (2025)
- ["When to Hand Off, When to Work Together": Understanding Concurrent Human-Agent Interaction in Shared Co-Creative Workspaces](https://mlsanity.com/paper/2603.02050/) (2026)
- [When Discourse Stalls: Moving Past Five Semantic Stopsigns about Generative AI in Design Research](https://mlsanity.com/paper/2503.08565/) (2025)
- [ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes](https://mlsanity.com/paper/2506.14832/) (2025)
- [GRAPHIC--Guidelines for Reviewing Algorithmic Practices in Human-centred Design and Interaction for Creativity](https://mlsanity.com/paper/2511.17443/) (2025)
- [Exploring Learners' Expectations and Engagement When Collaborating with Constructively Controversial Peer Agents](https://mlsanity.com/paper/2601.13479/) (2026)
- [Mixed-Initiative Context: Structuring and Managing Context for Human-AI Collaboration](https://mlsanity.com/paper/2604.07121/) (2026)
- [Brief2Design: A Multi-phased, Compositional Approach to Prompt-based Graphic Design](https://mlsanity.com/paper/2604.11019/) (2026)

## Abstract (arXiv)

Creative design is a nonlinear process where designers generate diverse ideas in the pursuit of an open-ended goal and converge towards consensus through iterative remixing. In contrast, AI-powered design tools often employ a linear sequence of incremental and precise instructions to approximate design objectives. Such operations violate customary creative design practices and thus hinder AI agents' ability to complete creative design tasks. To explore better human-AI co-design tools, we first summarize human designers' practices through a formative study with 12 design experts. Taking graphic design as a representative scenario, we formulate a nonlinear human-AI co-design framework and develop a proof-of-concept prototype, OptiMuse. We evaluate OptiMuse and validate the nonlinear framework through a comparative study. We notice a subconscious change in people's attitudes towards AI agents, shifting from perceiving them as mere executors to regarding them as opinionated colleagues. This shift effectively fostered the exploration and reflection processes of individual designers.