Guidance Source Matters: How Guidance from AI, Expert, or a Group of Analysts Impacts Visual Data Preparation and Analysis
Arpit Narechania, Alex Endert, Atanu R Sinha

TL;DR
This study investigates how the perceived source of guidance—AI, human expert, or group—affects user perception and usage during data analysis, revealing that source influences user experience and perceived benefit.
Contribution
It provides empirical evidence on the impact of guidance source on user perception and behavior in data analysis, highlighting differences between AI and human guidance.
Findings
Users perceive guidance source as influential on their experience.
AI guidance leads to higher reported benefit and regret.
Source affects how users utilize guidance at different analysis stages.
Abstract
The progress in generative AI has fueled AI-powered tools like co-pilots and assistants to provision better guidance, particularly during data analysis. However, research on guidance has not yet examined the perceived efficacy of the source from which guidance is offered and the impact of this source on the user's perception and usage of guidance. We ask whether users perceive all guidance sources as equal, with particular interest in three sources: (i) AI, (ii) human expert, and (iii) a group of human analysts. As a benchmark, we consider a fourth source, (iv) unattributed guidance, where guidance is provided without attribution to any source, enabling isolation of and comparison with the effects of source-specific guidance. We design a five-condition between-subjects study, with one condition for each of the four guidance sources and an additional (v) no-guidance condition, which…
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