Smart glasses enhance interactions with the environment by using head-mounted cameras to observe the user’s viewpoint, but lack the visual feedback used for common interactions. We introduce “Gazeify then Voiceify”, a multimodal approach allowing object selection via gaze and voice using displayless smart glasses. Users can select a physical object with their gaze, and the system generates a digital mask and a voice description of the object’s semantics. Users can further correct errors through free-form conversation. To demonstrate our approach, we develop an interactive system by integrating advanced object segmentation and detection with a visual-language model. User studies reveal that participants achieve correct gaze selection in 53% of the task trials and use voice disambiguation to correct 58% remaining errors. Participants also rated the system as likable, useful and easy to use.

Explainable AI (XAI) offers solutions to the challenges of predictability and interpretability in adaptive interfaces, particularly in Augmented Reality (AR) systems that dynamically adapt information based on situational contexts. While traditional XAI methods highlight contextual factors influencing adaptations, they often overlook the user’s internal understanding, such as their expertise and contextual perceptions. This omission can result in explanations that feel redundant or obvious. We present XAIUI, a computational approach that generates tailored explanations by integrating the system’s adaptation model with a Bayesian model of the user’s internal representation. Two online studies evaluated XAIUI. In the first study (N = 77), participants ranked XAIUI ’s explanations as most preferred compared to four ablations ( \(\chi^{2}(4)=62.28, {\textrm{p}} < 0.001\) ). In the second study (N = 110), XAIUI ’s explanations were rated significantly less complex ( \(\chi^{2}(4)=840.855, {\textrm{p}} < 0.001\) ) than all ablations, except showing no explanation. Our results demonstrate XAIUI ’s ability to deliver user-centric, concise, and intuitive explanations, highlighting its potential to enhance AI-driven interfaces.

We develop a probabilistic approach to understanding user preferences for adaptive placement of augmented reality (AR) interfaces in the physical environment through a series of user studies conducted using simulated desktop and virtual reality (VR) environments. From the first online crowdsourcing study and its validation in VR, we derived a set of potential factors behind user preferences for AR interface adaptation by assessing user-created layouts and analysing subjective user feedback. Building on this prior knowledge, we implemented a probabilistic optimisation system to generate adapted AR interfaces. Using generated layout pairs that prioritise different factors, we conducted a second online crowdsourcing study (N = 250) to elicit user preference rating data to quantify posterior probabilities for the weighting coefficients of the factors in the optimisation utility function. Overall, we found that the overall structures of layouts, such as shape and distribution, are more important to users than adapting to specific features of the environment, such as semantic associations between AR widgets and objects in the physical environments. We contribute a statistical model containing probabilistic distributions of different factors as a universal prior model that represents user preferences for AR interface placement that adapts to changing physical environments. Based on the results, we distil concrete guidelines for future adaptive AR interface systems regarding layout consistency, structure, and relationships between virtual widgets and physical objects.

Extended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We introduce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and experience data. MineXR enables elicitation of personalized interfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the environment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces.

Advances in ubiquitous computing have enabled end-user authoring of context-aware policies (CAPs) that control smart devices based on specific contexts of the user and environment. However, authoring CAPs accurately and avoiding run-time errors is challenging for end-users as it is difficult to foresee CAP behaviors under complex real-world conditions. We propose Fast-Forward Reality, an Extended Reality (XR) based authoring workflow that enables end-users to iteratively author and refine CAPs by validating their behaviors via simulated unit test cases. We develop a computational approach to automatically generate test cases based on the authored CAP and the user’s context history. Our system delivers each test case with immersive visualizations in XR, facilitating users to verify the CAP behavior and identify necessary refinements. We evaluated Fast-Forward Reality in a user study (N=12). Our authoring and validation process improved the accuracy of CAPs and the users provided positive feedback on the system usability.

The task of inputting text within virtual reality has attracted significant research attention over the last five years. Less well explored is the related task of correcting inputted text when errors are made. This is despite the fact that considerable time and frustration stems from efforts to correct text. In this paper, we bridge this gap in prior research and explore efficient methods for supporting text input correction in virtual reality. We present a characterization of the types and frequencies of errors encountered when inputting text in virtual reality and an analysis of effective editing strategies. We also present the results of a user study evaluating the performance and usability trade-offs for several interaction methods leveraging the unique capabilities of modern head-mounted displays.

Adaptive user interfaces (AUIs) can improve user experience by automatically adapting how information and functionality are presented in a user interface. However, the dynamic nature and potentially numerous variations of AUIs make them challenging to author. In this paper, we present a generalized framework for defining adaptation as interpolations between UIs and introduce a computational approach for intelligently generating new variations of a UI from a small set of designs. Based on this approach, we develop FrameKit, an authoring tool with a programming-by-example interface that retains flexibility and control afforded by manual authoring while reducing effort through automatic generation. We demonstrate that FrameKit can support adaptations that typically require domain-specific toolkits, such as those found in context-aware applications, responsive UIs, and ability-based adaptation. We evaluated FrameKit with ten front-end developers, who successfully authored AUIs after a short tutorial session and suggested that FrameKit provides an effective mental model for AUI authoring.

We introduce SonoHaptics, an audio-haptic cursor for gaze-based 3D object selection. SonoHaptics addresses challenges around providing accurate visual feedback during gaze-based selection in Extended Reality (XR), e. g., lack of world-locked displays in no- or limited-display smart glasses and visual inconsistencies. To enable users to distinguish objects without visual feedback, SonoHaptics employs the concept of cross-modal correspondence in human perception to map visual features of objects (color, size, position, material) to audio-haptic properties (pitch, amplitude, direction, timbre). We contribute data-driven models for determining cross-modal mappings of visual features to audio and haptic features, and a computational approach to automatically generate audio-haptic feedback for objects in the user’s environment. SonoHaptics provides global feedback that is unique to each object in the scene, and local feedback to amplify differences between nearby objects. Our comparative evaluation shows that SonoHaptics enables accurate object identification and selection in a cluttered scene without visual feedback.

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses when, what, and how to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users’ preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR’s utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.

Affordance refers to the perception of possible actions allowed by an object. Despite its relevance to human–computer interaction, no existing theory explains the mechanisms that underpin affordance-formation; that is, how affordances are discovered and adapted via interaction. We propose an integrative theory of affordance-formation based on the theory of reinforcement learning in cognitive sciences. The key assumption is that users learn to associate promising motor actions to percepts via experience when reinforcement signals (success/failure) are present. They also learn to categorize actions (e.g., “rotating” a dial), giving them the ability to name and reason about affordance. Upon encountering novel widgets, their ability to generalize these actions determines their ability to perceive affordances. We implement this theory in a virtual robot model, which demonstrates human-like adaptation of affordance in interactive widgets tasks. While its predictions align with trends in human data, humans are able to adapt affordances faster, suggesting the existence of additional mechanisms.

Annotated datasets of application GUIs contain a wealth of information that can be used for various purposes, from providing inspiration to designers and implementation details to developers to assisting end-users during daily use. However, users often struggle to formulate their needs in a way that computers can understand reliably. To address this, we study how people may interact with such GUI datasets using natural language. We elicit user needs in a survey (N = 120) with three target groups (designers, developers, end-users), providing insights into which capabilities would be useful and how users formulate queries. We contribute a labelled dataset of 1317 user queries, and demonstrate an application of a conversational assistant that interprets these queries and retrieves information from a large-scale GUI dataset. It can (1) suggest GUI screenshots for design ideation, (2) highlight details about particular GUI features for development, and (3) reveal further insights about applications. Our findings can inform design and implementation of intelligent systems to interact with GUI datasets intuitively.

Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user – for example, due to surprise or relearning effort – or “trap” the process to a suboptimal design immaturely. However, effects on users are hard to predict as they depend on factors that are latent and evolve over the course of interaction. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy: It finds beneficial changes when there are such and avoids changes when there are none. Our model-based reinforcement learning method plans sequences of adaptations and consults predictive HCI models to estimate their effects. We present empirical and simulation results from the case of adaptive menus, showing that the method outperforms both a non-adaptive and a frequency-based policy.

During the design of graphical user interfaces (GUIs), one typical objective is to ensure compliance with pertinent style guides, ongoing design practices, and design systems. However, designing compliant layouts is challenging, time-consuming, and can distract creative thinking in design. This paper presents a method for interactive layout transfer, where the layout of a source design – typically an initial rough working draft – is transferred automatically using a selected reference/template layout while complying with relevant guidelines. Our integer programming (IP) method extends previous work in two ways: first, by showing how to transform a rough draft into the final target layout using a reference template and, second, by extending IP-based approaches to adhere to guidelines. We demonstrate how to integrate the method into a real-time interactive GUI sketching tool. Evaluation results are presented from a case study and from an online experiment where the perceived quality of layouts was assessed.

The promise of on-body interactions has led to widespread development of wearable displays. They manifest themselves in highly variable shapes and form, and are realized using technologies with fundamentally different properties. Through an extensive survey of the field of wearable displays, we characterize existing systems based on key qualities of displays and wearables, such as location on the body, intended viewers or audience, and the information density of rendered content. We present the results of this analysis in an open, web-based interactive design space that supports exploration and refinement along various parameters. The design space, which currently encapsulates 129 cases of wearable displays, aims to inform researchers and practitioners on existing solutions and designs, and enable the identification of gaps and opportunities for novel research and applications. Further, it seeks to provide them with a thinking tool to deliberate on how the displayed content should be adapted based on key design parameters. Through this work, we aim to facilitate progress in wearable displays, informed by existing solutions, by providing researchers with an interactive platform for discovery and reflection.

Grid layouts are used by designers to spatially organise user interfaces when sketching and wireframing. However, their design is largely time consuming manual work. This is challenging due to combinatorial explosion and complex objectives, such as alignment, balance, and expectations regarding positions. This paper proposes a novel optimisation approach for the generation of diverse grid-based layouts. Our mixed integer linear programming (MILP) model offers a rigorous yet efficient method for grid generation that ensures packing, alignment, grouping, and preferential positioning of elements. Further, we present techniques for interactive diversification, enhancement, and completion of grid layouts. These capabilities are demonstrated using GRIDS, a wireframing tool that provides designers with real-time layout suggestions. We report findings from a ratings study (N = 13) and a design study (N = 16), lending evidence for the benefit of computational grid generation during early stages of design.

In domains where users are exposed to large variations in visuo-spatial features among designs, they often spend excess time searching for common elements (features) on an interface. This article contributes individualised predictive models of visual search, and a computational approach to restructure graphical layouts for an individual user such that features on a new, unvisited interface can be found quicker. It explores four technical principles inspired by the human visual system (HVS) to predict expected positions of features and create individualised layout templates: (I) the interface with highest frequency is chosen as the template; (II) the interface with highest predicted recall probability (serial position curve) is chosen as the template; (III) the most probable locations for features across interfaces are chosen (visual statistical learning) to generate the template; (IV) based on a generative cognitive model, the most likely visual search locations for features are chosen (visual sampling modelling) to generate the template. Given a history of previously seen interfaces, we restructure the spatial layout of a new (unseen) interface with the goal of making its features more easily findable. The four HVS principles are implemented in Familiariser, a web browser that automatically restructures webpage layouts based on the visual history of the user. Evaluation of Familiariser (using visual statistical learning) with users provides first evidence that our approach reduces visual search time by over 10%, and number of eye-gaze fixations by over 20%, during web browsing tasks.

This paper presents SAM, a modular and extensible JavaScript framework for self-adapting menus on webpages. SAM allows control of two elementary aspects for adapting web menus: (1) the target policy, which assigns scores to menu items for adaptation, and (2) the adaptation style, which specifies how they are adapted on display. By decoupling them, SAM enables the exploration of different combinations independently. Several policies from literature are readily implemented, and paired with adaptation styles such as reordering and highlighting. The process---including user data logging---is local, offering privacy benefits and eliminating the need for server-side modifications. Researchers can use SAM to experiment adaptation policies and styles, and benchmark techniques in an ecological setting with real webpages. Practitioners can make websites self-adapting, and end-users can dynamically personalise typically static web menus.

In domains where users are exposed to large variations in visuo-spatial features among designs, they often spend excess time searching for common elements (features) in familiar locations. This paper contributes computational approaches to restructuring layouts such that features on a new, unvisited interface can be found quicker. We explore four concepts of familiarisation, inspired by the human visual system (HVS), to automatically generate a familiar design for each user.

This paper studies a novel concept for integrating real-time design optimisation to a sketching tool. Although optimisation methods can attack very complex design problems, their insistence on precise objectives and a point optimum is a poor fit with sketching practices. Sketchplorer is a multitouch sketching tool that uses a real-time layout optimiser. It automatically infers the designer's task to search for both local improvements to the current design and global (radical) alternatives. Using predictive models of sensorimotor performance and perception, these suggestions steer the designer toward more usable and aesthetic layouts without overriding the designer or demanding extensive input.

We present PaperPulse, a design and fabrication approach that enables designers without a technical background to produce standalone interactive paper artifacts by augmenting them with electronics. With PaperPulse, designers overlay pre-designed visual elements with widgets available in our design tool. PaperPulse provides designers with three families of widgets designed for smooth integration with paper, for an overall of 20 different interactive components. We also contribute a logic demonstration and recording approach, Pulsation, that allows for specifying functional relationships between widgets. Using the final design and the recorded Pulsation logic, PaperPulse generates layered electronic circuit designs, and code that can be deployed on a microcontroller. By following automatically generated assembly instructions, designers can seamlessly integrate the microcontroller and widgets in the final paper artifact.

Using the space above desktop input devices adds a rich new input channel to desktop interaction. Input in this elevated layer has been previously used to modify the granularity of a 2D slider, navigate layers of a 3D body scan above a multitouch table and access vertically stacked menus. However, designing these interactions is challenging because the lack of haptic and direct visual feedback easily leads to input errors. For bare finger input, the user's fingers needs to reliably enter and stay inside the interactive layer, and engagement techniques such as midair clicking have to be disambiguated from leaving the layer. These issues have been addressed for interactions in which users operate other devices in midair, but there is little guidance for the design of bare finger input in this space.