  Cognitive behavioral therapy (CBT) is a widely used therapeutic method for
guiding individuals toward restructuring their thinking patterns as a means of
addressing anxiety, depression, and other challenges. We developed a large
language model (LLM)-powered prompt-engineered socially assistive robot (SAR)
that guides participants through interactive CBT at-home exercises. We
evaluated the performance of the SAR through a 15-day study with 38 university
students randomly assigned to interact daily with the robot or a chatbot (using
the same LLM), or complete traditional CBT worksheets throughout the duration
of the study. We measured weekly therapeutic outcomes, changes in
pre-/post-session anxiety measures, and adherence to completing CBT exercises.
We found that self-reported measures of general psychological distress
significantly decreased over the study period in the robot and worksheet
conditions but not the chatbot condition. Furthermore, the SAR enabled
significant single-session improvements for more sessions than the other two
conditions combined. Our findings suggest that SAR-guided LLM-powered CBT may
be as effective as traditional worksheet methods in supporting therapeutic
progress from the beginning to the end of the study and superior in decreasing
user anxiety immediately after completing the CBT exercise.
