  While the rise of large language models (LLMs) has created rich new
opportunities to learn about digital technology, many on the margins of this
technology struggle to gain and maintain competency due to lexical or
conceptual barriers that prevent them from asking appropriate questions.
Although there have been many efforts to understand factuality of LLM-created
content and ability of LLMs to answer questions, it is not well understood how
unclear or nonstandard language queries affect the model outputs. We propose
the creation of a dataset that captures questions of digital newcomers and
outsiders, utilizing data we have compiled from a decade's worth of one-on-one
tutoring. In this paper we lay out our planned efforts and some potential uses
of this dataset.
