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

The proliferation of artificial intelligence (AI) in interactive systems has led to significant challenges in model integration, but also end-user-related aspects such as over- and undertrust. This paper explores how multiple AI models with the same performance and behavior but different internal workings –a phenomenon called model multiplicity– affect system integration and user interaction. We discuss the implications of model multiplicity for transparency, trust, and operational effectiveness in interactive software systems.

In this poster we propose an implementation of direct feedforward for the ViRgilites system. The project defines two alternative uses, with respect to the current implementation, that only shows in an indirect way (icons, target object images, text) how to perform an interaction in the simulated environment. The first representation is a single avatar mode where the user sees a virtual avatar performing an action in the same environment as the user, while the second representation is a multiple avatar mode, where the user can choose to compare two interactions and see the avatar representations side by side in dedicated panels. We report on the initial ideas and proof-of-concepts, while we envision further modifications and a future evaluation of the final outcome.

Welcome to this issue of the Proceedings of the ACM on Human-Computer Interaction, bringing together contributions from the community on Engineering Interactive Computing Systems (EICS). The EICS track of the PACM-HCI is the primary venue for research contributions at the intersection of Human-Computer Interaction (HCI) and Software Engineering. This year, over the three rounds of submissions, for the issue of PACM-HCI we received 68 valid submissions (out of 90 submissions in total), of which we carefully selected 19 papers, bringing our acceptance rate to 27.9%. The result of this selection process is presented in this issue of the Proceedings of the ACM.