Data Has Entered the Chat: How Data Workers Conduct Exploratory Visual Analytic Conversations with GenAI Agents
Bibliographic record
Abstract
We investigate the potential of leveraging the code-generating capabilities of Large Language Models (LLMs) to support exploratory visual analysis (EVA) via conversational user interfaces (CUIs). We developed a technology probe that was deployed through two studies with a total of 50 data workers to explore the structure and flow of visual analytic conversations during EVA. We analyzed conversations from both studies using thematic analysis and derived a state transition diagram summarizing the conversational flow between four states of participant utterances ( Analytic Tasks , Editing Operations , Elaborations and Enrichments , and Directive Commands ) and two states of Generative AI (GenAI) agent responses (visualization, text). We describe the capabilities and limitations of GenAI agents according to each state and transitions between states as three co-occurring loops: analysis elaboration, refinement, and explanation. We discuss our findings as future research trajectories to improve the experiences of data workers using GenAI. The code and data are available at https://osf.io/6wxpa .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".