Machine learning systems are often inspected through 2D projections of high-dimensional representations using techniques such as t-SNE or UMAP. While these visualizations provide useful overviews of clustering and similarity, they are inherently static: they display only the existing data points and do not allow users to interactively explore a model's decision space. We present an interactive exploration system that uses a Variational Autoencoder (VAE) as a generative proxy over a model's training distribution, turning the latent space into a navigable workspace for model sensemaking. Unlike static embeddings, the proxy provides an explicit decoding path from latent coordinates to inputs, enabling interaction patterns such as continuous sampling, interpolation between anchors, and region probing. We operationalize these capabilities through a set of interactive probes that augment a familiar scatter-plot overview with generative overlays for comparing transitions between classes and examining sparsely populated regions. A within-subject formative study (N=16) comparing an interactive VAE-based method to a static t-SNE baseline shows that generative interaction substantially improves counterfactual reasoning and influences how users assess model behavior in sparse or uncertain regions, while static embeddings sometimes provide clearer boundary perception. From these findings, we derive concrete design guidelines and architectural considerations for engineering interactive AI model exploration systems using generative latent representations.

Virtual Reality (VR) environments present significant obstacles for users due to the sheer diversity of input devices, numerous interaction modalities, and varying user interface designs, which contribute to a steep learning curve. To address these complexities, feedforward is essential as it informs the user about the anticipated result of their actions, easing the learning process through contextualized previews of required interactions. However, designing and implementing effective direct feedforward, particularly without dedicated tools, can be tedious. We introduce BeatriXR, a comprehensive, reusable, and adaptive toolkit that provides extensive support for creating all possible configurations of direct feedforward to enhance user understanding and performance in VR. BeatriXR is an integrated system combining a modular VR toolkit with intelligent support derived from Large Language Models (LLMs). It supports the creation, visualization, and customization of direct feedforward using virtual avatars and two visualizations: an in-world representation and an on-screen comparison of interaction alternatives. This toolkit is mapped onto an established feedforward design space, covering phases such as Triggering, Previewing, and Exiting. To overcome the challenge designers face in determining optimal configurations, BeatriXR integrates an LLM-based adaptive decision support layer that proposes context-sensitive configuration alternatives. This guidance can be used both at design time to help domain experts select optimal configurations, and at runtime to adapt to the user's context and environment, such as recommending a change from a default partial avatar preview to a full ghosted avatar when a trainee shows uncertainty. We conducted an exploratory review with XR domain experts who rated the UI interface the final user can use to modify the feedforward settings, and the output of four LLM models customised for use in BeatriXR that would interact with procedure creators, providing insights on how to improve the UI experience and LLM answers. The results indicated that none of the evaluated models consistently outperformed the others, suggesting that the tested LLMs can be used interchangeably. Additionally, participants' feedback provided valuable insights for improving both the user interface, generally perceived positively, and the quality of the LLM-generated responses.

Astronauts routinely train spacewalks when on Earth. These spacewalks—extravehicular activities (EVAs)—are typically trained in neutral‑buoyancy pools or VR environments. However, neither environment captures the chaotic micro‑dynamics of a tethered tool in micro‑gravity. We designed and developed ZeroTraining: an encountered‑type haptic training rig (ZeroArm) paired with a VR simulation (ZeroPGT) that recreates the physical behavior of a tethered floating object in space. The integration of virtual and physical interactions supports dexterity training and improves transferability to real situations. We demonstrate feasibility using low‑cost components and validate the design in a formative study with ten participants.

We present an approach, AI-Spectra, to leverage model multiplicity for interactive systems. Model multiplicity means using slightly different AI models yielding equally valid outcomes or predictions for the same task, thus relying on many simultaneous "expert advisors" that can have different opinions. Dealing with multiple AI models that generate potentially divergent results for the same task is challenging for users to deal with. It helps users understand and identify AI models are not always correct and might differ, but it can also result in an information overload when being confronted with multiple results instead of one. AI-Spectra leverages model multiplicity by using a visual dashboard designed for conveying what AI models generate which results while minimizing the cognitive effort to detect consensus among models and what type of models might have different opinions. We use a custom adaptation of Chernoff faces for AI-Spectra; Chernoff Bots. This visualization technique lets users quickly interpret complex, multivariate model configurations and compare predictions across multiple models. Our design is informed by building on established Human-AI Interaction guidelines and well know practices in information visualization. We validated our approach through a series of experiments training a wide variation of models with the MNIST dataset to perform number recognition. Our work contributes to the growing discourse on making AI systems more transparent, trustworthy, and effective through the strategic use of multiple models.

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