DuoZone: A User-Centric, LLM-Guided Mixed-Initiative XR Window Management System
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Published on: December 5, 2014
Abstract:
Extended Reality (XR) offers portable, private, and extensive workspaces by extending the virtual workspace beyond desktops. However, current XR window setup and controls remain manual, increasing interaction costs during workspace setup and refinement. We propose DuoZone, an AI-assisted scaffolding system that helps users quickly set up an XR workspace and improves speed and efficiency in XR window controls. In DuoZone, users author spatial zones where the automation operates. Given a high-level goal, a large language model (LLM) recommends relevant applications, while a cost model adjusts window ordering and sizing to fit the workspace. An ablation study showed DuoZone provided a more readable and functional workspace setup than an LLM. A user study (N=16) with Apple's Vision Pro further found DuoZone's zone-mediated layout reduced time and workload for window adjustments, while AI-assisted zones reduced initial workspace setup time and workload relative to zones-only setup. These findings provide initial empirical evidence for an "AI scaffolds, user perfects" workflow in short-term XR workspace interaction. More broadly, this work illustrates how user-authored spatial structures can serve as an effective boundary between human intent and AI automation in future collaborative XR interfaces.
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