  Interactive Artificial Intelligent(AI) assistant systems are designed to
offer timely guidance to help human users to complete a variety tasks. One of
the remaining challenges is to understand user's mental states during the task
for more personalized guidance. In this work, we analyze users' mental states
during task executions and investigate the capabilities and challenges for
large language models to interpret user profiles for more personalized user
guidance.
