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Computer Science > Human-Computer Interaction

arXiv:2310.16164 (cs)
[Submitted on 24 Oct 2023]

Title:Conversational Challenges in AI-Powered Data Science: Obstacles, Needs, and Design Opportunities

Authors:Bhavya Chopra, Ananya Singha, Anna Fariha, Sumit Gulwani, Chris Parnin, Ashish Tiwari, Austin Z. Henley
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Abstract:Large Language Models (LLMs) are being increasingly employed in data science for tasks like data preprocessing and analytics. However, data scientists encounter substantial obstacles when conversing with LLM-powered chatbots and acting on their suggestions and answers. We conducted a mixed-methods study, including contextual observations, semi-structured interviews (n=14), and a survey (n=114), to identify these challenges. Our findings highlight key issues faced by data scientists, including contextual data retrieval, formulating prompts for complex tasks, adapting generated code to local environments, and refining prompts iteratively. Based on these insights, we propose actionable design recommendations, such as data brushing to support context selection, and inquisitive feedback loops to improve communications with AI-based assistants in data-science tools.
Comments: 24 pages, 8 figures
Subjects: Human-Computer Interaction (cs.HC)
Cite as: arXiv:2310.16164 [cs.HC]
  (or arXiv:2310.16164v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2310.16164

Submission history

From: Bhavya Chopra [view email]
[v1] Tue, 24 Oct 2023 20:19:07 UTC (10,065 KB)
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