  We introduce and test the System Dynamics Bot, a computer program leveraging
a large language model to automate the creation of causal loop diagrams from
textual data. To evaluate its performance, we ensembled two distinct databases.
The first dataset includes 20 causal loop diagrams and associated texts sourced
from the system dynamics literature. The second dataset comprises responses
from 30 participants to a vignette, along with causal loop diagrams coded by
three system dynamics modelers. The bot uses textual data and successfully
identifies approximately sixty percent of the links between variables and
feedback loops in both datasets. This paper outlines our approach, provides
examples, and presents evaluation results. We discuss encountered challenges
and implemented solutions in developing the System Dynamics Bot. The bot can
facilitate extracting mental models from textual data and improve model
building processes. Moreover, the two datasets can serve as a testbed for
similar programs.
