"I Just Need GPT to Refine My Prompts": Rethinking Onboarding and Help-Seeking with Generative 3D Modeling Tools

Abstract

Learning to use feature-rich software is a persistent challenge, but generative AI tools promise to lower this barrier by replacing complex navigation with natural language prompts. We investigated how people approach prompt-based tools for 3D modeling in an observational study with 26 participants (14 casuals, 12 professionals). Consistent with earlier work, participants skipped tutorials and manuals, relying on trial and error. What differed in the generative AI context was how and why they sought support: the prompt box became the entry point for learning, collapsing onboarding into immediate action, while some casual users turned to external LLMs for prompts. Professionals used 3D expertise to refine iterations and critically evaluated outputs, often discarding models that did not meet their standards, whereas casual users settled for "good enough." We contribute empirical insights into how generative AI reshapes help-seeking, highlighting new practices of onboarding, recursive AI-for-AI support, and shifting expertise in interpreting outputs.

Figures (4)

• Figure 1: Example of a Generative 3D modeling tool (Meshy AI) used in the study. Its key features include: (A) Prompt input panel for describing the desired 3D model, including style and generation settings; (B) Built-in AI help option that refines vague prompts into detailed, structured prompts for more accurate generation; (C) Built-in library of example prompts providing ready-to-use templates for diverse modeling tasks; (D) Built-in tutorial link offering step-by-step guidance on using the tool effectively; (E) Credit and upgrade indicators showing remaining credits and subscription options. Spline AI interface was similar to this interface as well. Despite help and examples built into the UI, they were largely ignored by participants, many preferring to go to ChatGPT instead.
• Figure 2: Target reference models provided to participants for modeling tasks. (a) Task 1: A glass-top wooden table (adapted from kiani2019beyond); (b) Task 2: A desk lamp with an adjustable arm. These tasks were designed consulting experts in 3D modeling and were designed in target generative AI tool (Meshy or Spline).
• Figure 3: Workflow visualizations of three participants illustrating the distinct approaches to onboarding and iterating with the generative 3D modeling tool. Left (P06–Professional): Linear path where the the user did not take any external or in-built help. Center (P10–Casual): The casual user cycled across multiple help-seeking resources, including tool documentation (docs), Google search engines (GSE), while iteratively generating prompts and evaluating outputs. This pathway demonstrates a "trial-and-error" orientation, with heavy reliance on external aids to understand of both prompting and tool capabilities. Right (P08–Casual): An AI-chaining approach where the participant depended on ChatGPT to draft and refine prompts before using them in the generative application, highlighting how casual users offloaded the burden of prompt crafting to external AI. Together, these three trajectories capture the major approaches in our study: (1) Self Formulated prompts, (2) Exploration scaffolded by external documentation and search, and (3) AI-for-AI prompting, where users relied on generative models to learn how to interact with other generative systems.
• Figure 4: Generalized workflows of participants. (a) Casual participants often treated generated models as immediately ready for 3D printing, overlooking issues such as surface anomalies or structural stability. (b) Professional participants followed two distinct pathways: a post-editing pathway, where they repaired or refined AI-generated models before considering them print-ready, and a model-from-scratch pathway, where they rejected the AI output, returned to ideation, and created new models for downstream use. These contrasting workflows illustrate how casual users emphasized immediacy and usability, while professionals foregrounded production quality and long-term integration.