2018
DOI: 10.1080/09540091.2018.1432566
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Experience evaluations for human–computer co-creative processes – planning and conducting an evaluation in practice

Abstract: 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, Outc… Show more

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Cited by 11 publications
(13 citation statements)
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“…In both experimental phases, participants filled in a 7-point Likert scale post-task questionnaire for user-experience evaluation whose questions represented the following metrics as defined by Kantosalo and Riihiaho ( 2019 ): Enjoyment, Expressivity, Outcome satisfaction, Ease of use, Collaboration, Ownership, Exploration, Immersion, and Productivity. Table 1 presents the questions corresponding to each metric both for the simple harmonization and for the computationally assisted task.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…In both experimental phases, participants filled in a 7-point Likert scale post-task questionnaire for user-experience evaluation whose questions represented the following metrics as defined by Kantosalo and Riihiaho ( 2019 ): Enjoyment, Expressivity, Outcome satisfaction, Ease of use, Collaboration, Ownership, Exploration, Immersion, and Productivity. Table 1 presents the questions corresponding to each metric both for the simple harmonization and for the computationally assisted task.…”
Section: Methodsmentioning
confidence: 99%
“…The interest in the impact of technology on human creativity is more recent (Lubart, 2005 ; Shneiderman et al, 2006 ; Shneiderman, 2007 ) in comparison to research on the definition and evaluation of creativity itself (see, Cherry and Latulipe, 2014 ). However, creativity support has already been studied in the context of various creative human activities such as poetry writing (Kantosalo and Riihiaho, 2019 ), creative design (Albert and Runco, 1999 ; Bonnardel and Zenasni, 2010 ), 3D modeling (Chaudhuri and Koltun, 2010 ), or general problem solving (Massetti, 1996 ).…”
Section: Introductionmentioning
confidence: 99%
“…One approach in computational creativity is so‐called human–computer co‐creativity (e.g., Davis, 2021; Feldman, 2017), with an aim to ‘facilitate human creativity via computationally creative means and vice versa’ (Kantosalo, 2019, p. 1). Whereas earlier computational agents were mostly seen as tools, in recent studies, creative AI has been considered more of a medium or partner to humans (Davis, 2021; Elgammal & Mazzone, 2020; Kantosalo & Toivonen, 2016). This is also the case with art‐generating AI algorithms that, according to Mazzone and Elgammal (2019), are closer to a medium than just inanimate objects or tools that artists use.…”
Section: Geography and The Spatialities Of Creativitymentioning
confidence: 99%
“…However, techno‐material perspectives on creativity have been less investigated within creative geographies (except for: Nordström, 2017; Rose, 2016; Woodward et al, 2015; Zebracki & Luger, 2019). We claim that AI expands the understandings of creativity due to its partly autonomous capacity to create novel outcomes together with humans (Davis, 2021; Feldman, 2017; Kantosalo & Toivonen, 2016). We ask what role AI plays in such creativity, what its spatial manifestations are, and what directions creative geographies can take in the age of AI.…”
Section: Introductionmentioning
confidence: 99%
“…Machine-in-the-Loop: Human-machine collaboration for the enhancement of creative writing has been examined under automated assistance Gordon, 2015, 2018), co-authorship (Tucker, 2019), co-creativity (Manjavacas et al, 2017;Kantosalo and Riihiaho, 2019;Calderwood et al, 2020), interactive storytelling (Swanson and Gordon, 2012;Brahman et al, 2020) and machinein-the-loop (Clark et al, 2018;Akoury et al, 2020).…”
Section: Modelmentioning
confidence: 99%