Teleportation - changing the point of view in 3D space by specifying a position - is one of the most common locomotion solutions in VR. However, it currently lacks a mechanism to adjust the height in 3D space, and it is difficult for users to predict the exact final view after the teleportation. Users are relocated to a place without knowing what the final view will look like. As a result, they often need to perform remedial interactions to achieve their ideal position, which can be time-consuming and effort-intensive. In this paper, we present Fly the Moon to Me (Locomoontion), a novel technique that enables users to bring their destination to themselves through object manipulation. Users first create a copy of the object they want to approach as a preview by selecting it, then bring it to an ideal position and direction using existing object manipulation techniques, and then snap the original object to the preview together with the rest of the world. A controlled experiment with 18 participants via a teleportation task reveals that Locomoontion is more effective than the traditional Point&Teleport technique with grabbing the world as a remedy to adjust the final positioning.

Recent advances in language models have significantly expanded the capabilities of AI-powered conversational agents. Nonetheless, current technology is still primarily designed with monolingual English speakers in mind, overlooking the need of more personalized agents by multilingual users. Particularly, prior work showed that multilingual individuals preferred conversational agents that accommodate their desired multilingual style. However, these approaches rely on probabilistic methods to automatically determine the agent’s multilingual style, which often fails to align with the needs of multilingual users, as their preferences are nuanced, ad hoc, and difficult to predict. In our work, we explore user control of multilingual style as a step toward developing a mixed-initiative multilingual conversational agent tailored to the needs of multilingual users. We first derived design considerations and dimensions of user control from a formative study with 10 participants. Next, we implemented Mirrios, a prototypical conversational system with multilingual style control, and used it as a probe to conduct an user study with 12 participants. We identified preferred designs for multilingual style control and found that this control reduced the need to constrain language habits, accommodated ad hoc language needs, and enabled more personalized interactions with conversational agents. Based on our findings, we propose design implications to inform the design of multilingual style control and future mixed-initiative multilingual conversational agents.

Voice messages, by nature, prevent users from gauging the emotional tone without fully diving into the audio content. This hinders the shared emotional experience at the pre-retrieval stage. Research scarcely explored “Emotional Teasers”—pre-retrieval cues offering a glimpse into an awaiting message’s emotional tone without disclosing its content. We introduce EmoWear, a smartwatch voice messaging system enabling users to apply 30 animation teasers on message bubbles to reflect emotions. EmoWear eases senders’ choice by prioritizing emotions based on semantic and acoustic processing. EmoWear was evaluated in comparison with a mirroring system using color-coded message bubbles as emotional cues (N=24). Results showed EmoWear significantly enhanced emotional communication experience in both receiving and sending messages. The animated teasers were considered intuitive and valued for diverse expressions. Desirable interaction qualities and practical implications are distilled for future design. We thereby contribute both a novel system and empirical knowledge concerning emotional teasers for voice messaging.

This paper adopted an iterative design process to gain insights into programmers’ strategies when using LLMs for programming. We proposed CoLadder, a novel system that supports programmers by facilitating hierarchical task decomposition, direct code segment manipulation, and result evaluation during prompt authoring. A user study with 12 experienced programmers showed that CoLadder is effective in helping programmers externalize their problem-solving intentions flexibly, improving their ability to evaluate and modify code across various abstraction levels, from their task’s goal to final code implementation.