How People Prompt Generative AI to Create Interactive VR Scenes
Authors
Generative AI tools can provide people with the ability to create virtual environments and scenes with natural language prompts. Yet, \textit{how} people will formulate such prompts is unclear---particularly when they inhabit the environment that they are designing. For instance, it is likely that a person might say, ``Put a chair here,'' while pointing at a location. If such linguistic and embodied features are common to people's prompts, we need to tune models to accommodate them. In this work, we present a Wizard of Oz elicitation study with 22 participants, where we studied people's implicit expectations when verbally prompting such programming agents to create interactive VR scenes. Our findings show when people prompted the agent, they had several implicit expectations of these agents: (1) they should have an embodied knowledge of the environment; (2) they should understand embodied prompts by users; (3) they should recall previous states of the scene and the conversation, and that (4) they should have a commonsense understanding of objects in the scene. Further, we found that participants prompted differently when they were prompting \textit{in situ} (i.e. within the VR environment) versus \textit{ex situ} (i.e. viewing the VR environment from the outside). To explore how these lessons could be applied, we designed and built Ostaad, a conversational programming agent that allows non-programmers to design interactive VR experiences that they inhabit. Based on these explorations, we outline new opportunities and challenges for conversational programming agents that create VR environments.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 67%
PANDALens: Towards AI-Assisted In-Context Writing on OHMD During Travels
CHI '24· Generative AI (Text, Image, Music, Video) +1
- 67%
Exploring User Experiences with Generative AI-Reconstructed Daily Photos
DIS '25· Generative AI (Text, Image, Music, Video) +1
Based on Jaccard similarity of research subtopics & professions (≥60%)