In mixed-initiative systems, the mode of AI assistance delivery can be as consequential as the assistance itself. We investigated two assistance delivery modes: on-demand help (users request via Button) and pre-scheduled help (assistance delivered at user-selected intervals, with user actions resetting the Timer). To evaluate these modes, we selected Rush Hour puzzles as the human–AI collaborative task because they capture elements of real-world problem solving such as analysis, resource management, and decision-making under constraints. To enhance ecological validity, we imposed monetary costs for both time and AI assistance, simulating scenarios where people must balance implicit or explicit trade-offs such as time pressure, financial limitations, or opportunity costs. Although task performance was comparable across modes, participants who used the pre-scheduled (Timer) mode reported more positive perceptions of the AI, even when their ending budget was low. This suggests that assistance delivery mode can shape user experience independent of task outcomes, indicating that human-AI systems may need to consider how AI assistance is delivered alongside improving task performance.

As AR applications expand across our daily lives, understanding user interactions within mixed environments-where virtual and physical objects coexist-has become increasingly important. This work investigates human performance and behavior during visual search and selection tasks across three object conditions: (1) virtual objects only, (2) physical objects only, and (3) a combination of virtual and physical objects (Mixed) requiring frequent virtuality switching. We also vary the distance to the target plane while maintaining subtended visual angle: a ‘near’ condition at the headset's focal plane and a ‘far’ condition at a mid-zone action space distance of 3 meters. Results indicate that, while there are some small effects that can be linked back to established display phenomena such as Vergence-Accommodation Conflict, a main cause for performance differences among the object conditions comes from people adjusting their search and selection behavior to the challenges of virtuality switching, resulting in Mixed conditions requiring significant longer completion times, associated with significantly larger head motion, eye movement, and controller movement. Mixed conditions also resulted in significantly lower accuracy for target selection. Virtual-to-Physical transitions took the longest to complete, followed by Physical-to-Virtual transitions, both significantly longer than transitions to targets within the same virtuality. Participants also reported increased Eye Strain, Fatigue, and Task Load with the Mixed conditions. This work provides insight into the complexities of mixed object interaction and presents quantitative assessments of pronounced virtuality switching, with implications for designing effective AR interfaces.

Everyday objects, like remote controls or electric toothbrushes, are crafted with hand-accessible interfaces. Expanding on this design principle, extended reality (XR) interfaces for physical tasks could facilitate interaction without necessitating the release of grasped tools, ensuring seamless workflow integration. While established data, such as hand anthropometric measurements, guide the design of handheld objects, XR currently lacks comparable data, regarding reachability, for single-hand interfaces while grasping objects. To address this, we identify critical design factors and a design space representing grasp-proximate interfaces and introduce a simulation tool for generating reachability and displacement cost data for designing these interfaces. Additionally, using the simulation tool, we generate a dataset based on grasp taxonomy and common household objects. Finally, we share insights from a design workshop that emphasizes the significance of reachability and motion cost data, empowering XR creators to develop bespoke interfaces tailored specifically to grasping hands.