  Humans' trust in AI constitutes a pivotal element in fostering a synergistic
relationship between humans and AI. This is particularly significant in the
context of systems that leverage AI technology, such as autonomous driving
systems and human-robot interaction. Trust facilitates appropriate utilization
of these systems, thereby optimizing their potential benefits. If humans
over-trust or under-trust an AI, serious problems such as misuse and accidents
occur. To prevent over/under-trust, it is necessary to predict trust dynamics.
However, trust is an internal state of humans and hard to directly observe.
Therefore, we propose a prediction model for trust dynamics using dynamic
structure equation modeling, which extends SEM that can handle time-series
data. A path diagram, which shows causalities between variables, is developed
in an exploratory way and the resultant path diagram is optimized for effective
path structures. Over/under-trust was predicted with 90\% accuracy in a drone
simulator task,, and it was predicted with 99\% accuracy in an autonomous
driving task. These results show that our proposed method outperformed the
conventional method including an auto regression family.
