For those who don't know (how) to ask: Building a dataset of technology questions for digital newcomers

Evan Lucas, Kelly Steelman, Leo Ureel, Charles Wallace

Track: Innovations in AI for Education (Day 1)

Paper Length: short-paper (2 pages + references)

Keywords: Digital Literacy Tutoring, Linguistic Uncertainty, Large Language Models, Data Mining, Question Understanding, Question Generation

TL;DR: To support automated tutoring support for digital newcomers, a new dataset of technology-related questions formed from a nonexpert perspective is proposed.

Abstract: 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.

Submission Number: 64