  A growing number of tools have used Large Language Models (LLMs) to support
developers' code understanding. However, developers still face several barriers
to using such tools, including challenges in describing their intent in natural
language, interpreting the tool outcome, and refining an effective prompt to
obtain useful information. In this study, we designed an LLM-based
conversational assistant that provides a personalized interaction based on
inferred user mental state (e.g., background knowledge and experience). We
evaluate the approach in a within-subject study with fourteen novices to
capture their perceptions and preferences. Our results provide insights for
researchers and tool builders who want to create or improve LLM-based
conversational assistants to support novices in code understanding.
