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

Davis, Nicholas; Hsiao, Chih-Pin; Singh, Kunwar Yashraj; Li, Lisa; Magerko, Brian

doi:10.1145/2856767.2856795

Cited by 80 publications

(49 citation statements)

References 34 publications

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“…The system also asked the designer to reflect on the currently chosen images and their relation to the current mood board. These proactive questions were perceived as disrupting by some, similarly to the slider manipulations presented by the Drawing Apprentice [10]. However, we also received positive feedback indicating that these active questions can support the reflection on design choices -e.g., as a reminder that there are more dimensions that one might consider.…”

Section: Discussionmentioning

confidence: 77%

“…We aim for an approach that, similarly, is able to lead -if the designer so desires -and has explanations available upon request. Other exploratory drawing agents, such as the Drawing Apprentice [10], use a turn-taking approach to draw with an artist, while exploration/exploitation is steered by means of sliders. We also draw from the idea of active participation via instant feedback.…”

Section: Related Workmentioning

confidence: 99%

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May AI?

Koch

Lucero

Hegemann

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Design ideation is a prime creative activity in design. However, it is challenging to support computationally due to its quickly evolving and exploratory nature. The paper presents cooperative contextual bandits (CCB) as a machine-learning method for interactive ideation support. A CCB can learn to propose domain-relevant contributions and adapt their exploration/exploitation strategy. We developed a CCB for an interactive design ideation tool that 1) suggests inspirational and situationally relevant materials ("may AI?"); 2) explores and exploits inspirational materials with the designer; and 3) explains its suggestions to aid reflection. The application case of digital mood board design is presented, wherein visual inspirational materials are collected and curated in collages. In a controlled study, 14 of 16 professional designers preferred the CCB-augmented tool. The CCB approach holds promise for ideation activities wherein adaptive and steerable support is welcome but designers must retain full outcome control. CCS CONCEPTS • Human-centered computing → Interactive systems and tools; • Computing methodologies → Machine learning; • Applied computing → Arts and humanities.

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Section: Discussionmentioning

confidence: 77%

Section: Related Workmentioning

confidence: 99%

May AI?

Koch

Lucero

Hegemann

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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“…We have seen evidence of how collaboration leads to dynamic and emergent meaning structures that inspire novel ideas during empirical studies of pretend play [7] and collaborative drawing [8]. We have also found how collaborators often provide unexpected ideas and thus lead to surprising results.…”

Section: Introductionmentioning

confidence: 80%

Creative Sense-Making

Davis

Hsiao

Singh

et al. 2017

Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition

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This paper describes a new technique for quantifying interaction dynamics during open-ended co-creation, such as collaborative drawing or playing pretend. We present a cognitive framework called creative sense-making. This framework synthesizes existing cognitive science theories and empirical investigations into open-ended improvisation to develop a method of quantifying cognitive states and types of interactions through time. We apply this framework to empirical studies of human collaboration (in the domain of pretend play) and AI-based systems (in the domain of collaborative drawing) to establish its validity through cross-domain application and inter-rater reliability within each domain. The creative sense-making framework described includes a qualitative coding technique, interaction coding software, and the cognitive theory behind their application.

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“…Kantosalo, Toivanen, & Toivonen, 2015;Bown, 2015;Jacob & Magerko, 2015;Davis, Hsiao, Yashraj Singh, Li, & Magerko, 2016). The term user experience refers to a number of qualitative and hedonistic aspects of interaction, including for example "the result of enjoyable interactions and/or anticipated interactions with a product" (Lachner, Naegelein, Kowalski, Spann, & Butz, 2016).…”

Section: Introductionmentioning

confidence: 99%

Experience evaluations for human–computer co-creative processes – planning and conducting an evaluation in practice

Kantosalo

Riihiaho

2018

Connection Science

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In human-computer co-creativity, humans and creative computational algorithms create together. Too often, only the creative algorithms and their outcomes are evaluated when studying these co-creative processes, leaving the human participants to little attention. This paper presents a case study emphasising the human experiences when evaluating the use of a co-creative poetry writing system called the Poetry Machine. The co-creative process was evaluated using seven metrics: Fun, Enjoyment, Expressiveness, Outcome satisfaction, Collaboration, Ease of writing, and Ownership. The metrics were studied in a comparative setting using three co-creation processes: a human-computer, a human-human, and a human-human-computer co-creation process. Twelve pupils of age 10-11 attended the studies in six pairs trying out all the alternative writing processes. The study methods included observation in paired-user testing, questionnaires, and interview. The observations were complemented with analyses of the video recordings of the evaluation sessions. According to statistical analyses, Collaboration was the strongest in human-human-computer co-creation, and weakest in human-computer co-creation. Ownership was just the opposite: weakest in human-human-computer co-creation, and strongest in human-computer co-creation. Other metrics did not produce statistically significant results. In addition to the results, this paper presents the lessons learned in the evaluations with children using the selected methods. ARTICLE HISTORY

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

Cited by 80 publications

References 34 publications

May AI?

May AI?

Creative Sense-Making

Experience evaluations for human–computer co-creative processes – planning and conducting an evaluation in practice

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