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

arXiv cs.HCby Kanak Gautam, Poorvi Bhatia, Parmit K. ChilanaApril 1, 20261 min read0 views
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arXiv:2603.29118v1 Announce Type: new 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 ou

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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.

Comments: 16 pages, 10 figures, CHI 2026 submission

Subjects:

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

ACM classes: H.5.2; I.3.6

Cite as: arXiv:2603.29118 [cs.HC]

(or arXiv:2603.29118v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2603.29118

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Kanak Gautam [view email] [v1] Tue, 31 Mar 2026 01:09:27 UTC (2,383 KB)

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