I Lead, You Help but Only with Enough Details

Oh, Changhoon; Song, Jungwoo; Choi, Jinhan; Kim, Seonghyun; Lee, Sung‐Woo; Suh, Bongwon

doi:10.1145/3173574.3174223

Cited by 170 publications

(46 citation statements)

References 22 publications

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“…For example, Gill [38] argues that the user experience of interactive applications using collaborative intelligence and distributed creativity should be further explored to improve these systems. In particular, the effects of computational or AI feedback on the creativity and experience of users will be investigated by various studies e. g. [22], [23], [24], [25]. While effective feedback can help to improve creative performance, an improvement process in complex fields still takes much time, as it is often difficult for users to deduce the exact cause of an error from the feedback.…”

Section: Content-based Analysis Of Resultsmentioning

confidence: 99%

User Experience of Creativity Support Tools – A Literature Review in a Management Context

Sengewald¹,

Roth²

2020

WI2020 Zentrale Tracks

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Creativity Support Tools or Creativity Support Systems are a wellstudied subject in the research of computer science and information systems. Whether the User Experience of Creativity Support Tools or Creativity Support Systems has been likewise sufficiently researched, shall be examined. A systematic literature search was conducted to investigate the current state of scientific literature. This review provides a brief overview of open and superfluous research areas in the field of User Experience for the development of Creativity Support Tools for business or management purposes. A total of 19 papers were selected and coded, sorted to concepts and assigned to the various phases of the creative process. The results of the analysis have shown that there is a need for further research in this field.

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Section: Content-based Analysis Of Resultsmentioning

confidence: 99%

User Experience of Creativity Support Tools – A Literature Review in a Management Context

Sengewald¹,

Roth²

2020

WI2020 Zentrale Tracks

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“…The growing use of AI across a range of application domains has led to an increased focus on usable [9,39], fair [49,53], and transparent [4] systems within the HCI community as well as the wider public debate [33,50]. The successful uptake and deployment of these algorithmic systems does, however, rely on the incorporation of stakeholders' knowledge and feedback [62].…”

Section: Discussionmentioning

confidence: 99%

Designing Visual Markers for Continuous Artificial Intelligence Support

Berkel

Ahmad

Stoyanov

et al. 2020

ACM Trans. Comput. Healthcare

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Colonoscopy, the visual inspection of the large bowel using an endoscope, offers protection against colorectal cancer by allowing for the detection and removal of pre-cancerous polyps. The literature on polyp detection shows widely varying miss rates among clinicians, with averages ranging around 22%--27%. While recent work has considered the use of AI support systems for polyp detection, how to visualise and integrate these systems into clinical practice is an open question. In this work, we explore the design of visual markers as used in an AI support system for colonoscopy. Supported by the gastroenterologists in our team, we designed seven unique visual markers and rendered them on real-life patient video footage. Through an online survey targeting relevant clinical staff ( N = 36), we evaluated these designs and obtained initial insights and understanding into the way in which clinical staff envision AI to integrate in their daily work-environment. Our results provide concrete recommendations for the future deployment of AI support systems in continuous, adaptive scenarios.

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“…In collabdraw, the human and AI alternate strokes in a turn-byturn manner. In contrast, an example of a continuous drawing system is DuetDraw (Oh et al, 2018). This system integrates sketch-rnn's capabilities into a variety of tools that the AI can utilize with varying initiative in collaborative drawing.…”

Section: Co-creative Drawing Systemsmentioning

confidence: 99%

“…In contrast, a low initiative AI may be required to wait for a prompt from the artist before it can contribute to the drawing. The level of initiative might be set by the artist, such as in the DuetDraw (Oh et al, 2018). 3.…”

Section: Interaction Factorsmentioning

confidence: 99%

“…This completion might be based on an explicit model that the AI uses of the drawing, such as the animal categories in the collabdraw system (Fan et al, 2019). It may attempt to identify and complete what the artist is drawing as in the DuetDraw system (Oh et al, 2018). The DuetDraw system also allows the artist to have the AI colorize the drawing, which is a form of completion as well.…”

Section: Completionmentioning

confidence: 99%

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Exploring Co-creative Drawing Workflows

Jansen

Sklar

2021

Front. Robot. AI

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This article presents the outcomes from a mixed-methods study of drawing practitioners (e.g., professional illustrators, fine artists, and art students) that was conducted in Autumn 2018 as a preliminary investigation for the development of a physical human-AI co-creative drawing system. The aim of the study was to discover possible roles that technology could play in observing, modeling, and possibly assisting an artist with their drawing. The study had three components: a paper survey of artists' drawing practises, technology usage and attitudes, video recorded drawing exercises and a follow-up semi-structured interview which included a co-design discussion on how AI might contribute to their drawing workflow. Key themes identified from the interviews were (1) drawing with physical mediums is a traditional and primary way of creation; (2) artists' views on AI varied, where co-creative AI is preferable to didactic AI; and (3) artists have a critical and skeptical view on the automation of creative work with AI. Participants' input provided the basis for the design and technical specifications of a co-creative drawing prototype, for which details are presented in this article. In addition, lessons learned from conducting the user study are presented with a reflection on future studies with drawing practitioners.

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I Lead, You Help but Only with Enough Details

Cited by 170 publications

References 22 publications

User Experience of Creativity Support Tools – A Literature Review in a Management Context

User Experience of Creativity Support Tools – A Literature Review in a Management Context

Designing Visual Markers for Continuous Artificial Intelligence Support

Exploring Co-creative Drawing Workflows

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