# Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems arXiv: https://arxiv.org/abs/2305.07465 ยท HTML: https://mlsanity.com/paper/2305.07465/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Builds on 17 of its 41 references that are in the map. - First posted on arXiv 3 May 2023. ## Builds on (most cited first, 17 of 17) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021) - [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022) - [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022) - [Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization](https://mlsanity.com/paper/2210.01241/) (2022) - [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021) - [PCGRL: Procedural Content Generation via Reinforcement Learning](https://mlsanity.com/paper/2001.09212/) (2020) - [Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing](https://mlsanity.com/paper/2210.13669/) (2022) - [Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems](https://mlsanity.com/paper/2204.07666/) (2022) - [Language Models are Few-shot Learners for Prognostic Prediction](https://mlsanity.com/paper/2302.12692/) (2023) - [Evaluating Mixed-initiative Conversational Search Systems via User Simulation](https://mlsanity.com/paper/2204.08046/) (2022) - [An Interaction Framework for Studying Co-Creative AI](https://mlsanity.com/paper/1903.09709/) (2019) - [Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes](https://mlsanity.com/paper/2104.04039/) (2021) - [Cut the CARP: Fishing for zero-shot story evaluation](https://mlsanity.com/paper/2110.03111/) (2021) - [Creative Wand: A System to Study Effects of Communications in Co-Creative Settings](https://mlsanity.com/paper/2208.02886/) (2022) - [Explainable Computational Creativity](https://mlsanity.com/paper/2205.05682/) (2022) - [Story Designer: Towards a Mixed-Initiative Tool to Create Narrative Structures](https://mlsanity.com/paper/2210.09294/) (2022) ## Built on by (most cited first, 20 of 20) - [Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers](https://mlsanity.com/paper/2311.09180/) (2023) - [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024) - [CoQuest: Exploring Research Question Co-Creation with an LLM-based Agent](https://mlsanity.com/paper/2310.06155/) (2023) - [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023) - [Next Steps for Human-Centered Generative AI: A Technical Perspective](https://mlsanity.com/paper/2306.15774/) (2023) - [Inkspire: Supporting Design Exploration with Generative AI through Analogical Sketching](https://mlsanity.com/paper/2501.18588/) (2025) - [An Ontology of Co-Creative AI Systems](https://mlsanity.com/paper/2310.07472/) (2023) - [Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows](https://mlsanity.com/paper/2502.18737/) (2025) - [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024) - [Finance Language Model Evaluation (FLaME)](https://mlsanity.com/paper/2506.15846/) (2025) - [Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration](https://mlsanity.com/paper/2509.10652/) (2025) - [Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents](https://mlsanity.com/paper/2602.01405/) (2026) - [Human-Centered AI Communication in Co-Creativity: An Initial Framework and Insights](https://mlsanity.com/paper/2505.18385/) (2025) - [Vidmento: Creating Video Stories Through Context-Aware Expansion With Generative Video](https://mlsanity.com/paper/2601.22013/) (2026) - [Human Aesthetic Preference-Based Large Text-to-Image Model Personalization: Kandinsky Generation as an Example](https://mlsanity.com/paper/2402.06389/) (2024) - [Thinking Outside the (Gray) Box: A Context-Based Score for Assessing Value and Originality in Neural Text Generation](https://mlsanity.com/paper/2502.13207/) (2025) - [Do Language Models Agree with Human Perceptions of Suspense in Stories?](https://mlsanity.com/paper/2508.15794/) (2025) - [Beyond Following: Mixing Active Initiative into Computational Creativity](https://mlsanity.com/paper/2409.16291/) (2024) - [Real-Time Intuitive AI Drawing System for Collaboration: Enhancing Human Creativity through Formal and Contextual Intent Integration](https://mlsanity.com/paper/2508.19254/) (2025) - [Towards Cognitive Process-Aware Proactive Writing Support](https://mlsanity.com/paper/2608.30424/) (2026) ## Abstract (arXiv) Generative Artificial Intelligence systems have been developed for image, code, story, and game generation with the goal of facilitating human creativity. Recent work on neural generative systems has emphasized one particular means of interacting with AI systems: the user provides a specification, usually in the form of prompts, and the AI system generates the content. However, there are other configurations of human and AI coordination, such as co-creativity (CC) in which both human and AI systems can contribute to content creation, and mixed-initiative (MI) in which both human and AI systems can initiate content changes. In this paper, we define a hypothetical human-AI configuration design space consisting of different means for humans and AI systems to communicate creative intent to each other. We conduct a human participant study with 185 participants to understand how users want to interact with differently configured MI-CC systems. We find out that MI-CC systems with more extensive coverage of the design space are rated higher or on par on a variety of creative and goal-completion metrics, demonstrating that wider coverage of the design space can improve user experience and achievement when using the system; Preference varies greatly between expertise groups, suggesting the development of adaptive, personalized MI-CC systems; Participants identified new design space dimensions including scrutability -- the ability to poke and prod at models -- and explainability.