🤖 AI Summary
This study investigates how users collaborate with generative AI to accomplish 3D modeling tasks when bypassing traditional tutorials. Through qualitative observations of 26 participants—comprising 14 non-experts and 12 professionals—the research examines their natural language–driven learning trajectories, help-seeking strategies, and approaches to evaluating outputs. Three key findings emerge: (1) a novel onboarding mechanism wherein the prompt input field itself serves as the primary entry point; (2) a new practice of recursively refining prompts using external large language models; and (3) a pronounced divergence between expert and non-expert users in interpreting and filtering AI-generated results. The findings indicate that users predominantly rely on trial-and-error and external AI assistance rather than formal instruction, highlighting an emergent paradigm of human-AI collaborative learning in generative environments.
📝 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.