"It's like a rubber duck that talks back": Understanding Generative AI-Assisted Data Analysis Workflows through a Participatory Prompting Study
arxiv(2024)
摘要
Generative AI tools can help users with many tasks. One such task is data
analysis, which is notoriously challenging for non-expert end-users due to its
expertise requirements, and where AI holds much potential, such as finding
relevant data sources, proposing analysis strategies, and writing analysis
code. To understand how data analysis workflows can be assisted or impaired by
generative AI, we conducted a study (n=15) using Bing Chat via participatory
prompting. Participatory prompting is a recently developed methodology in which
users and researchers reflect together on tasks through co-engagement with
generative AI. In this paper we demonstrate the value of the participatory
prompting method. We found that generative AI benefits the information foraging
and sensemaking loops of data analysis in specific ways, but also introduces
its own barriers and challenges, arising from the difficulties of query
formulation, specifying context, and verifying results.
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