  We claim that LLMs can be paired with formal analysis methods to provide
accessible, relevant feedback for HRI tasks. While logic specifications are
useful for defining and assessing a task, these representations are not easily
interpreted by non-experts. Luckily, LLMs are adept at generating
easy-to-understand text that explains difficult concepts. By integrating task
assessment outcomes and other contextual information into an LLM prompt, we can
effectively synthesize a useful set of recommendations for the learner to
improve their performance.
