--Janin Koch, and I present today on behalf of my courses
ImageSense, An Intelligent Collaborative Ideation
Tool to Support the Diverse Human-Computer Partnerships.
We're especially interested in visual ideation
for mood boards.
Moods boards are visual collabs composed of images, takes,
and objects that express concepts, ideas, and emotions
commonly used in creative fields,
such as design or fashion.
Searching for new ideas involves often relevance but also
serendipitous encounters.
In collaboration is key to extend ideas.
The non-verbal expression helps designers
create a flow and to discover new perspectives, which
includes reflection and debriefing.
However, this is not how current tool works.
Most of them are text-based, single-user tools.
And while composition tools are quite powerful,
they are not collaborative by nature.
So our research aims to explore new ways
to integrate contributions from human and intelligence agents,
identify appropriate kinds of intelligent assistance
for different forms of ideation, and identify differences
in how designers interact with remote, human collaborators
in contrast to intelligence assistants.
With that in mind, I introduce ImageSense, an integrated
collaborative ideation tool that supports
the entire creative process from material collecting,
composition, to reflection, communication,
which consists of a shared-space with other designers
in a private space for searching intelligence suggestion agents
and other reflection tools.
Designers can search material by using text, images, or both.
The semantic labels of the images are presented underneath
and can be edited to refine your results.
We provide centered-search engines
like Google Image Search, DuckDuckGo, but also
design engines like Unsplash as resources.
Image Cascade provides categorized, curated images
that appear similar to browsing through magazines
for inspiration.
These items can stop the flow to inspect and select
interesting images for the collage.
Suggest-o-matic is an intelligent agent
that suggests images based on the current mood board
style and semantics.
Designers can guide the agent's suggestions with requests,
like something more surprising or more like that.
The semantic relevance of the suggestions
are shown next to it.
The Tech Clouds allow designers to see overviews
of presented image semantics, common associations,
but also more abstract themes and provoking red-related color
connotations to help designers analyze and find new ideas.
We also enable designers to use colors, crop,
and abstract shapes to write a realistic design ideation tool,
while text and notes can help to express and communicate
ideas more clear.
We designed the study with nine professional designers,
a collaborative designer, and ImageSense intelligent tools,
using a fictional eco-car as a design brief.
Using ImageSense, the designers collected material together,
iterated over the composition, and reflected and discussed
ideas using a video-conferencing tool and ImageSense.
Additional text enabled to help to structure and communicate
the ideas.
We evaluated the study using the process-screen recordings ,
semi-structured interviews, and questionnaires,
using the thematic analysis and descriptive statistics.
Designers reported regarding our first research question
that ImageSense helped them to explore novel alternatives
and effectively communicate their design better
than currently by [?] tools.
It also was described to encourage
reflection of the composition itself,
as well as the traceability of the process.
Now, our second research question,
we try to identify appropriate kinds of intelligent assistance
for such a process.
ImageCascade was deemed especially
useful to extend ideas with images
that are not directly related and can't be actively searched.
Semantic search helped, in contrast,
to find search terms for hard-to-express visual ideas.
The AI suggestions were especially useful
when designers felt stuck or as an additional push to get
started.
Interacting with a human collaborator, in contrast,
helped them when the topics were more clear.
Participants knew what to look for and for [?]
general-content discussions.
Designers, overall, enjoyed being
able to choose minute-by-minute the types of interactions
they had with both human-design partners
and intelligent assistants.
In summary, we contributed a novel design of the Integrated
Human-AI collaboration.
We show how different ideation needs
correlate with different types of interaction
and present three forms of human-AI partnership approaches
using Designer-led, AI-led, and shared agency.
Thank you for your attention.
And I'm looking forward to your questions.