As virtual 3D environments become more prevalent, equitable access is essential for blind and low-vision (BLV) users, who face challenges with spatial awareness, navigation, and interaction. Prior work has explored supplementing visual information with auditory or haptic modalities, but these methods are static and offer limited support for dynamic, in-context adaptation. Recent advances in generative AI allow users to query and modify 3D scenes via natural language, introducing a paradigm that offers greater flexibility and control for accessibility. We present RAVEN, a system that enables BLV users to issue queries and modification prompts to improve the runtime accessibility of 3D virtual scenes. We evaluated RAVEN with eight BLV people and six Unity developers, generating empirical insights into how conversational programming can support personalized accessibility in 3D environments. Our work highlights both the promise of natural language interaction—intuitive, flexible, and empowering—and the challenges of ensuring reliability, transparency, and trust in generative AI–driven accessibility systems.

Figure 1: RAVEN is an interactive system that empowers BLV users to query and modify 3D scenes via natural language.The above image illustrates an example of an accessibility modification: A) A low-vision user types in a modification text.B) The system integrates runtime code generation LLM agent with dynamic scene information and instructions to apply accessibilityenhancing changes at runtime.C) The system compiles LLM-produced code to achieve modification while providing spoken response to the user.

Children around 5 years old exhibit a strong inclination to help others. However, they show less helping behavior when others are present, which is known as the bystander effect. As social robots enter children’s lives, children’s helping behavior may also be affected by social robots. Therefore, the purpose of this study is to find out whether children also exhibit bystander effects when confronted with a distressed robot, and its underlying factors contributing to this phenomenon. Sixty participants (Mage = 66.10 months, SD = 4.46) were randomly assigned to three conditions: alone condition, bystander condition and bystander-unavailable condition. Children’s helping behavior was observed while interacting with the robot recipient, and then they participated in an animacy interview. The results revealed that children can attribute animacy to the robot, however, in bystander condition, their helping behavior was significantly less than in other conditions when confronted with a robot recipient, mainly due to the diffusion of responsibility.

Previous VR sound accessibility work substituted sounds with visual or haptic output to increase VR accessibility for deaf and hard of hearing (DHH) people. However, deafness occurs on a spectrum, and many DHH people (e.g., those with partial hearing) can also benefit greatly from having more control over the audio instead of substituting it with another modality. In this paper, we explore the possibilities of modifying sounds in VR to support DHH people. To understand the best modification features for this goal, we designed and implemented 18 VR sound modification tools spanning four categories, including prioritizing sounds, modifying sound parameters, providing spatial assistance, and adding additional sounds. We evaluated our tools in five diverse VR scenarios with 10 DHH people, finding that our tool can improve DHH users’ VR experience, but could be further improved by providing more customization options and decreasing distraction. We then compiled a Unity toolkit from select tools and conducted a preliminary evaluation with six Unity VR developers. Findings show that our toolkit is easy to use and debug but could be enhanced through modularization and better documentation. We close by discussing further implications of sound modification in VR.

To increase VR sound accessibility for deaf and hard of hearing users, previous work has substituted sounds with visual or haptic feedback. However, many DHH people (e.g., those with partial hearing) can also benefit from modifying audio (e.g., changing volume based on priorities) instead of fully substituting it with another modality. In this demo paper, we present a toolkit that allows modifying sounds in VR to support DHH people. We designed and implemented 18 VR sound modification tools spanning four categories, including prioritizing sounds, modifying sound parameters, providing spatial assistance, and adding additional sounds. We present five demo scenarios with tools incorporated, covering common VR use cases.

A common way to enable immersion in VR is to render a virtual body that mirrors the user’s physical movements. VR allows us to design interaction schemes that go beyond direct avatar embodiments. In particular, there is a growing body of literature investigating the simultaneous control of multiple bodies in VR. We contribute to this literature by investigating the important case where multiple bodies perform a coordinated interaction with each other. Such actions directly question what kind of embodiment users experience. Concretely, we investigate people’s abilities to perform coordinated bimanual selection and handoff tasks between a first-person and third-person body through a user study with 19 participants. Results provide quantitative & qualitative evidence for people’s ability to perform complex coordinated tasks through two bodies. Furthermore we characterize participant performance in different task and interaction configurations, summarize the strategies they employed, and discuss qualities of user’s proprioception.