| dc.description.abstract | When it comes to any problem, their causes, and solutions, people
often have very different perspectives, primarily due to the environment
in which they were raised (culture, education, socio-economic status,
and so forth). Complex problems often require coordinated actions from
all stakeholders to achieve a resolution. Agreeing on the same course
of action can sometimes be difficult, as the stakeholders might have a
different perspective of the specific problem. Causal map is a way to
capture different perspectives people have about any situation. Thus,
we posed the following research question - is it possible to use conver-
sational artificial intelligence to capture and store the thought process
of a particular problem? In this research, we have conducted an exper-
iment which consisted of two parts: 1) developing a model for a voice-
activated personal assistant that interacts, captures, and converts the
responses of the participant into causal maps and 2) a detailed pre-test
and post-test questionnaire that focuses on assessing interactions and
willingness of the participants to collaborate with the developed model.
We were able to build an Alexa skill that could successfully capture
participants thought process and transform it into a causal map that
could be analyzed along with data from other participants. The results
of our pre-test and post-test surveys conducted with ten researchers who
participated showed that they rated the Alexa skill as a useful tool for
capturing the thought process of a problem. In our view, understanding
the human thought process is crucial for stakeholders to agree on the
same course of resolution. The research concludes with a discussion of
future uses and limitations. | en_US |
