# AI-Supported EUD for Data Visualization: An Exploratory Case Study
## 4 Phase 2: Answering RQ2
### Results
#### 4.3.2 Findings for the Intermediate User  
Different themes emerged for the intermediate user.  
**Prompting encourages exploration of the system's capabilities.** The intermediate user was quicker in grasping how the system worked, readily trying different types of requests to better understand what the output was. Since the beginning, this user intuitively knew what to write and how to format the inputs to enter the desired measures. Engagement in trial and error was the main attitude to assess the capabilities of the system and to experiment with various request formats. Data viz choices included bar charts, line charts, pie charts, tables, and scatter plots. However, when attempting to design a scatter plot, the initial result was not the one intended, and the user soon realized that two measures were required (_\"I asked for a scatter plot to show the correlation between sales and number of orders, but then I realized I needed to define two separate measures, one for each axis\"_). After this attempt, the user proceeded with an alternative data viz.  
**Proactive management of the system's limitations**. Some frustration arose when the system failed to display expected data, such as a KPI. However, the user seemed to stick to the given tasks, trying to improve data viz independently, e.g., by modifying the granularity of the data, transforming monthly orders into weekly orders, and the like. This user proactively refined the analyses by introducing new measures and modifying requests as some insights developed during the interaction; for instance, after thinking about _\"Hmm, okay, but what if I wanted to ask, for example, to include the percentage variation?\"_, the user successfully obtained the output update.  
**Dealing with complex data visualization requests.** There were moments when the system did not immediately respond as expected. For example, when trying to design a heatmap but a bar chart was returned instead, and upon specifically asking, the user obtained the correct data viz. A second example was when a complex request was also attempted, highlighting both the difficulty in making the system become responsive in this respect and realizing how challenging it could become to articulate complex analytical needs. In general, when dealing with complex data visualizations, difficulties in fully expressing analytical intentions were expressed (_\"There's also a human limitation, obviously, when it comes to explaining things. I'm one of those people who knows exactly what I want in my head, but explaining it...\"_). Furthermore, there was uncertainty regarding the system's capabilities and limitations, leading to a continuous process of request exploration and subsequent refinement.  
**Misunderstandings addressed easily.** On the one hand, when faced with misunderstanding from the system, the user independently refined or rewrote prompts, adjusting the approach accordingly. On the other hand, when occasional input errors occurred, the user noticed that the system accurately interpreted their intent (_\"Oh, but it even corrected the name I mistyped. Nice, I got it\"_). In general, the response of the system was found to be clear, also because there was feedback even when the system was unable to perform the request.  
**Trust in the system.** The system was found to be adequate from the satisfaction and accuracy perspectives, but further refinements to the prototype were perceived as necessary. Therefore, trust in the system could be gained provided that further improvements in the interaction modalities are made (_\"I imagine these systems still need to be refined and trained, but the beginning looks promising. After a proper testing phase, yes, I could trust it\"_). Despite this, the tool was evaluated as substantially improving daily work.