Information Technology Reference
In-Depth Information
7.2.1
Creativity Support Tools
Shneiderman distinguishes creativity support tools (CSTs) from productivity sup-
port tools through three criteria: clarity of task domain and requirements, clarity of
success measures, and nature of the user base (
2007
). Productivity support tools are
designed around a clear task with known requirements, have well-defi ned success
metrics, and are characterized by a known and relatively well-understood set of
users. In contrast, CSTs often work in ill-defi ned domains that have unknown
requirements, vague success measures, and an unpredictable user base. For exam-
ple, consider productivity support tools for the well-defi ned goals of product supply
scheduling, which include many clearly defi ned variables like cost metrics for ship-
ping effi ciency. Contrast this with a drawing support tool, like ShadowDraw (Lee
et al.
2011
) or iCanDraw (Dixon et al.
2010
), that helps users learn drawing skills
and inspires creativity.
Creativity support tools can take many forms. Nakakoji (
2006
) organizes the
range of creativity support tools with three metaphors: running shoes, dumbbells,
and skis (Nakakoji
2006
). Running shoes improve the abilities of users to execute a
creative task they are already capable of; they improve the results users get from a
given set of abilities. Dumbbells support users learning about a domain to become
capable without the tool itself; they build users' knowledge and abilities. Skis pro-
vide users with new experiences of creative tasks that were previously impossible;
they enable new forms of execution. A contemporary text editor that highlights
grammar mistakes is a running shoe; explaining why those wordings are ungram-
matical makes the tool a dumbbell. Collaborative drawing tools would be a type of
ski because they enable a whole new class of creative expression where the user
collaborates with a computer. Nakakoji believes CSTs that introduce new creative
experiences to novices will gain popularity because of the positive impact novel
creative experiences can have on creative output (Nakakoji
2006
).
7.2.2
Generative Computational Creativity
The class of creative systems that autonomously produce creative products is
referred to here as generative computational creativity. This approach is largely
inherited from AI, and it dissects human creativity into observable behaviors such
as narrative, poetry, ideation, games, analogy, design, etc. These researchers then
create computational models for their tightly delineated creativity module with the
hope and effort to try to integrate those components with other embodied and situ-
ated aspects of creativity later.
The typical software architecture for generative computational creativity pro-
gresses as follows: The system fi rst “reads” or interprets a large corpus of material
into structured representations that it uses as its knowledge base. To make the sys-
tems more “creative,” the corpus is carefully selected to lead to more interesting
