mlsanityData to 13 Oct 2026

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

arXiv 2603.29118 ↗First posted 31 Mar 2026cs.HC cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 26 of its 87 references that are in the map. First posted on arXiv 31 Mar 2026.

Builds on

26 of 87 references.

References · 26

  1. 2021Zero-Shot Text-to-Image Generationcited by 4,284
  2. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,019
  3. 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,288
  4. 2023Instruction-Following Evaluation for Large Language Modelscited by 1,191
  5. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  6. 2023Shap-E: Generating Conditional 3D Implicit Functionscited by 383
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  11. 20223DALL-E: Integrating Text-to-Image AI in 3D Design Workflowscited by 41
  12. 2023How Knowledge Workers Think Generative AI Will (Not) Transform Their Industriescited by 30
14 more
  1. 2024Why and When LLM-Based Assistants Can Go Wrong: Investigating the Effectiveness of Prompt-Based Interactions for Software Help-Seekingcited by 26
  2. 2024The Effects of Generative AI on Computing Students' Help-Seeking Preferencescited by 23
  3. 2024Prompts Are Programs Too! Understanding How Developers Build Software Containing Promptscited by 19
  4. 2019An Empirical Study of Obsolete Answers on Stack Overflowcited by 14
  5. 2023Practices and Challenges of Using GitHub Copilot: An Empirical Studycited by 14
  6. 2023fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Taskscited by 10
  7. 2025Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Makingcited by 9
  8. 2025What Makes a Good Natural Language Prompt?cited by 6
  9. 2023Deep3DSketch: 3D modeling from Free-hand Sketches with View- and Structural-Aware Adversarial Trainingcited by 5
  10. 2025Expanding the Generative AI Design Space through Structured Prompting and Multimodal Interfacescited by 3
  11. 2024Progress and Prospects in 3D Generative AI: A Technical Overview including 3D humancited by 2
  12. 2024Invisible Labor in Open Source Software Ecosystemscited by 2
  13. 2024Digital Labor and the Inconspicuous Production of Artificial Intelligencecited by 1
  14. 2025"All Roads Lead to ChatGPT": How Generative AI is Eroding Social Interactions and Student Learning Communitiescited by 1

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