Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems

Abstract

Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-creative process. Therefore the interaction model is a critical and essential component for effective co-creative systems. There is relatively little research about interaction design in the co-creativity field, which is reflected in a lack of focus on interaction design in many existing co-creative systems. The primary focus of co-creativity research has been on the abilities of the AI. This paper focuses on the importance of interaction design in co-creative systems with the development of the Co-Creative Framework for Interaction design (COFI) that describes the broad scope of possibilities for interaction design in co-creative systems. Researchers can use COFI for modeling interaction in co-creative systems by exploring alternatives in this design space of interaction. COFI can also be beneficial while investigating and interpreting the interaction design of existing co-creative systems. We coded a dataset of existing 92 co-creative systems using COFI and analyzed the data to show how COFI provides a basis to categorize the interaction models of existing co-creative systems. We identify opportunities to shift the focus of interaction models in co-creativity to enable more communication between the user and AI leading to human-AI partnerships.

arXiv commentThis paper appears in the ACM Transactions on Computer-Human Interaction. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission from [email protected]

References in corpus (4)

• Model Cards for Model Reporting
• Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
• CharacterChat: Supporting the Creation of Fictional Characters through Conversation and Progressive Manifestation with a Chatbot
• Shimon the Rapper: A Real-Time System for Human-Robot Interactive Rap Battles

Cited by in corpus (16)

• The Creativity of Text-to-Image Generation
• Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries
• Understanding User Perceptions, Collaborative Experience and User Engagement in Different Human-AI Interaction Designs for Co-Creative Systems
• AIdeation: Designing a Human-AI Collaborative Ideation System for Concept Designers
• From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape
• DanceGen: Supporting Choreography Ideation and Prototyping with Generative AI
• Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature
• Beyond Productivity: Rethinking the Impact of Creativity Support Tools
• CreepyCoCreator? Investigating AI Representation Modes for 3D Object Co-Creation in Virtual Reality
• Toward AI standardization: A triadic human-ai collaboration framework for multi-level autonomous mobility
• Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-Design
• Understanding the Video Content Creation Journey of Creators with Sensory Impairment in Kenya
• Boosting Mixed-Initiative Co-Creativity in Game Design: A Tutorial
• From Daily Song to Daily Self: Supporting Reflective Songwriting of Deaf and Hard-of-Hearing Individuals through Generative Music AI
• No Pixel Left Behind: Filling Gaps in Anime Colorization
• A Systematic Review of Human-AI Co-Creativity