Virtual Reality (VR) environments challenge users with varied input devices, interaction methods, and interface designs, resulting in a steep learning curve. Feedforward "informs the user about what the result of his action will be", and it allows to ease the process of learning of the end user by providing ways to represent the required action to perform, using contextualised previews that show how to complete a given interaction. Yet, creating effective direct feedforward without specialised tools remains a tedious process. We present BeatriXR, a flexible and adaptive toolkit that simplifies the creation of direct-feedforward configurations in VR. It enables users to build, visualise, and customise direct feedforward using virtual avatars, offering both in-world representations and on-screen comparisons of interaction options. Mapped to the recognised design-space by Muresan et al. (including Triggering, Previewing, and Exiting phases), BeatriXR streamlines development and helps ensure effective feedforward integration in VR environments. As additional support for the feedforward design task, BeatriXR features an LLM-powered decision-support layer that suggests possible configuration options. This guidance helps designers select optimal settings during development for adapting the presentation to user needs and the expected configuration of the interaction context. We conducted an exploratory review with XR domain experts who rated the UI for modifying feedforward settings, and assessed four LLM models in the task of suggesting possible configuration options. The participants’ feedback was positive and provided valuable insights for improving both the user interface, which was generally perceived positively, and the quality of the LLM-generated responses.

Navigating the interaction landscape of Virtual Reality (VR) and Augmented Reality (AR) presents significant complexities due to the plethora of available input hardware and interaction modalities, compounded by spatially diverse visual interfaces. Such complexities elevate the likelihood of user errors, necessitating frequent backtracking. To address this, we introduce ViRgilites, a virtual guidance framework that delivers multi-level feedforward information covering the available interaction techniques as well as the future possibilities to interact with virtual objects, anticipating the interaction effects and how they fit with the overall user's goal. ViRgilites is engineered to facilitate task execution, empowering users to make informed decisions about action methodologies and alternative courses of action. This paper presents the architecture and functionality of ViRgilites and demonstrates its efficacy through evaluation with a formative user study.

Research on enabling novice AR/VR developers has emphasized the need to lower the technical barriers to entry. This is often achieved by providing new authoring tools that provide simpler means to implement XR interactions through abstraction. However, novices are then bound by the ceiling of each tool and may not form the correct mental model of how interactions are implemented. We present XRSpotlight, a system that supports novices by curating a list of the XR interactions defined in a Unity scene and presenting them as rules in natural language. Our approach is based on a model abstraction that unifies existing XR toolkit implementations. Using our model, XRSpotlight can find incomplete specifications of interactions, suggest similar interactions, and copy-paste interactions from examples using different toolkits. We assess the validity of our model with professional VR developers and demonstrate that XRSpotlight helps novices understand how XR interactions are implemented in examples and apply this knowledge in their projects.

This paper proposes a solution for supporting end users in configuring Virtual Reality environments by exploiting reusable templates created by experts. We identify the roles participating in the environment development and the means for delegating part of the behaviour definition to the end users. We focus in particular on enabling end users to define the environment behaviour. The solution exploits a taxonomy defining common virtual objects having high-level actions for specifying event-condition-action rules readable as natural language sentences. End users exploit such actions to define the environment behaviour. We report on a proof-of-concept implementation of the proposed approach, on its validation through two different case studies (virtual shop and museum), and on evaluating the approach with expert users.

Standard development libraries for Virtual and Mixed Reality support haptic feedback through low-level parameters, which do not guide developers in creating effective interactions. In this paper, we report some preliminary results on a simplified structure for the creation, assignment and execution of haptic feedback for standard controllers with the optional feature of synchronizing an haptic pattern to an auditory feedback. In addition, we present the results of a preliminary test investigating the users' ability in recognizing variations in intensity and/or duration of the stimulus, especially when the two dimensions are combined for encoding information.