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AI Drawing Partner: Co-Creative Drawing Agent and Research Platform to Model Co-Creation

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arxiv 2501.06607 v1 pith:ONXDQVWJ submitted 2025-01-11 cs.HC

classification cs.HC
keywords co-creativedrawinginteractionccsmpartnersense-makingsystemagent
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper describes the AI Drawing Partner, which is a co-creative drawing agent that also serves as a research platform to model co-creation. The AI Drawing Partner is an early example of a quantified co-creative AI system that automatically models the co-creation that happens on the system. The method the system uses to capture this data is based on a new cognitive science framework called co-creative sense-making (CCSM). The CCSM is based on the cognitive theory of enaction, which describes how meaning emerges through interaction with the environment and other people in that environment in a process of sense-making. The CCSM quantifies elements of interaction dynamics to identify sense-making patterns and interaction trends. This paper describes a new technique for modeling the interaction and collaboration dynamics of co-creative AI systems with the co-creative sense-making (CCSM) framework. A case study is conducted of ten co-creative drawing sessions between a human user and the co-creative agent. The analysis includes showing the artworks produced, the quantified data from the AI Drawing Partner, the curves describing interaction dynamics, and a visualization of interaction trend sequences. The primary contribution of this paper is presenting the AI Drawing Partner, which is a unique co-creative AI system and research platform that collaborates with the user in addition to quantifying, modeling, and visualizing the co-creative process using the CCSM framework.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Who Decides How Knowing Becomes Doing? Redistributing Authority in Human-AI Music Co-Creation

    cs.HC 2025-09 conditional novelty 5.0 of 10

    Editing an AI's reasoning steps instead of only typing prompts made 180 musicians report more contestability, agency, and protection of non-mainstream styles, though the survey evidence lacks statistical analysis.

  2. SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills

    cs.HC 2025-06 conditional novelty 5.0 of 10

    A four-stage AI illustration pipeline with a large-language-model tutor aims to scaffold novice drawing skill acquisition by exposing intermediate diffusion outputs.

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