Empirically Studying Participatory Sense-Making in Abstract Drawing with a Co-Creative Cognitive Agent

By Nicholas Mark Davis, Chih-Pin Hsiao, Kunwar Yashraj Singh, Lisa Li, Brian Magerko. 2016

163 citations recorded by the source corpus.

This paper reports on the design and evaluation of a co-creative drawing partner called the Drawing Apprentice, which was designed to improvise and collaborate on abstract sketches with users in real time. The system qualifies as a new genre of creative technologies termed "casual creators" that are meant to creatively engage users and provide enjoyable creative experiences rather than necessarily helping users make a higher quality creative product. We introduce the conceptual framework of participatory sense-making and describe how it can help model and understand open-ended collaboration. We report the results of a user study comparing human-human collaboration to human-computer collaboration using the Drawing Apprentice system. Based on insights from the user study, we present a set of design recommendations for co-creative agents.

Subfields: Mechanical Engineering, Human-Computer Interaction

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Citations in the corpus

AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts (2021, background): “In a collaboration, AI can play various roles, such as casual creators that encourage exploration [26] or assistants that compensate for humanweaknesses [41, 74].”
I Lead, You Help but Only with Enough Details: Understanding User Experience of Co-Creation with Artificial Intelligence (2018, background): “developed Drawing Apprentice, which can collaborate with users by analyzing their drawn input and responding in real time in improvisational interactions [9, 10].”
Wordcraft: Story Writing With Large Language Models (2022, background): “There have been many studies of humanAI co-creation across domains, including drawing [8, 27, 31], music [15, 22, 25], video game content creation [24], and design ideation [16].”
Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems (2022, background): “In this quote, De Jaegher and Di Paolo outlines the process of participatory sense-making where meaning-making of relational interaction dynamics such as the rhythm of turn-taking, manner of action, interaction style, etc is necessary [41].”
Mapping the Landscape of Creativity Support Tools in HCI (2019, unknown): “2018 [29, 30, 62, 73, 90, 111, 126, 162, 165, 171, 175, 184] 2017 [3, 7, 36, 43, 64, 82, 94, 98, 102, 118, 130, 134, 150, 158, 176, 183, 192] 2016 [9, 25, 35, 39, 57, 63, 66, 79, 81, 87, 101, 114, 117, 127, 129, 159, 167, 187] 2015 [38, 54, 89, 100, 123, 137, 160, 166, 168, 186] 2014 [14, 37, 55, 71”
Novice-AI Music Co-Creation via AI-Steering Tools for Deep Generative Models (2020, background): “The acceleration of AI capabilities has renewed interest in how AI can enable human-AI co-creation in domains such as drawing [36, 9, 32, 14], creative writing [18, 7], design ideation [33], video game content generation [23], and dance [31].”
May AI? Design Ideation with Cooperative Contextual Bandits (2019, methodology): “These proactive questions were perceived as disrupting by some, similarly to the slider manipulations presented by the Drawing Apprentice [10].”
CLIP-CLOP: CLIP-Guided Collage and Photomontage (2022, background): “…neural generators, including Generative Adversarial Networks (Brock, Donahue, and Simonyan 2018; Esser, Rombach, and Ommer 2021), Diffusion models (Dhariwal and Nichol 2021), evolution strategies on colored shapes (Tian and Ha 2021) or evolution-based Neural Visual Grammars (Fernando, Eslami, and…”
Design Principles for Generative AI Applications (2024, background): “Design for Co-Creation provides greater specifcity to generative AI’s unique capabilities for human-AI co-creation, which has been examined extensively within HCI communities (e.g., [34, 53, 77, 121, 129]).”