Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 871 publications
Preview abstract
Audio Description ( AD) provides essential access to visual media for blind and low vision ( BLV) audiences. Yet current AD production tools remain largely inaccessible to BLV video creators, who possess valuable expertise but face barriers due to visually- driven interfaces. We present ADCanvas, a multimodal authoring system that supports non- visual control
over audio description ( AD) creation. ADCanvas combines conversational interaction with keyboard- based playback control and a plain- text, screen reader–
accessible editor to support end- to- end AD authoring and visual question answering ( VQA). Combining screen- reader- friendly controls with a multimodal
LLM agent, ADCanvas supports live VQA, script generation, and AD modification. Through a user study with 12 BLV video creators, we find that users adopt
the conversational agent as an informational aide and drafting assistant, while maintaining agency through verification and editing. For example, participants
saw themselves as curators who received information from the model and filtered it down for their audience. Our findings offer design implications for
accessible media tools, including precise editing controls, accessibility support for creative ideation, and configurable rules for human- AI collaboration.
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Modern user interfaces are complex composites, with elements originating from various sources, such as the operating system, apps, a web browser, or websites. Many security and privacy models implicitly depend on users correctly identifying an element's source, a concept we term ''surface attribution.'' Through two large-scale vignette-based surveys (N=4,400 and N=3,057), we present the first empirical measurement of this ability.
We find that users struggle, correctly attributing UI source only 55% of the time on desktop and 53% on mobile. Familiarity and strong brand cues significantly improve accuracy, whereas UI positioning, a long-held security design concept especially for browsers, has minimal impact. Furthermore, simply adding a ''Security & Privacy'' brand cue to Android permission prompts failed to improve attribution. These findings demonstrate a fundamental gap in users' mental models, indicating that relying on them to distinguish trusted UI is a fragile security paradigm.
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A Framework for Interactive Machine Learning and Enhanced Conversational Systems
Jerry Young
Richard Abisla
Sanjay Batra
Mikki Phan
Nature, Springer-Verlag (2026)
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Conversational systems are increasingly prevalent, yet current versions often fail to support the full range of human speech, including variations in speed, rhythm, syntax, grammar, articulation, and resonance. This reduces their utility for individuals with dysarthria, apraxia, dysphonia, and other language and speech-related disabilities. Building on research that emphasizes the need for specialized datasets and model training tools, our study uses a scaffolded approach to understand the ideal model training and voice recording process. Our findings highlight two distinct user flows for improving model training and provide six guidelines for future conversational system-related co-design frameworks. This study offers important insights on creating more effective conversational systems by emphasizing the need to integrate interactive machine learning into training strategies.
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As artificial intelligence (AI) is rapidly integrated into healthcare, ensuring that this innovation helps to combat health inequities requires engaging marginalized communities in health AI futuring. However, little research has examined Black populations’ perspectives on the use of AI in health contexts, despite the widespread health inequities they experience–inequities that are already perpetuated by AI. Addressing this research gap, through qualitative workshops with 18 Black adults, we characterize participants’ cautious optimism for health AI addressing structural well-being barriers (e.g., by providing second opinions that introduce fairness into an unjust healthcare system), and their concerns that AI will worsen health inequities (e.g., through health AI biases they deemed inevitable and the problematic reality of having to trust healthcare providers to use AI equitably). We advance health AI research by articulating previously-unreported health AI perspectives from a population experiencing significant health inequities, and presenting key considerations for future work.
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The field of Human-Computer Interaction is approaching a critical inflection point, moving beyond the era of static, deterministic systems into a new age of self-evolving systems. We introduce the concept of Adaptive generative interfaces that move beyond static artifacts to autonomously expand their own feature sets at runtime. Rather than relying on fixed layouts, these systems utilize generative methods to morph and grow in real-time based on a user’s immediate intent. The system operates through three core mechanisms: Directed synthesis (generating new features from direct commands), Inferred synthesis (generating new features for unmet needs via inferred commands), and Real-time adaptation (dynamically restructuring the interface's visual and functional properties at runtime). To empirically validate this paradigm, we executed a within-subject (repeated measures) comparative study (N=72) utilizing 'Penny,' a digital banking prototype. The experimental design employed a counterbalanced Latin Square approach to mitigate order effects, such as learning bias and fatigue, while comparing Deterministic interfaces baseline against an Adaptive generative interfaces. Participant performance was verified through objective screen-capture evidence, with perceived usability quantified using the industry-standard System Usability Scale (SUS). The results demonstrated a profound shift in user experience: the Adaptive generative version achieved a System Usability Scale (SUS) score of 84.38 ('Excellent'), significantly outperforming the Deterministic version’s score of 53.96 ('Poor'). With a statistically significant mean difference of 30.42 points (p < 0.0001) and a large effect size (d=1.04), these findings confirm that reducing 'navigation tax' through adaptive generative interfaces directly correlates with a substantial increase in perceived usability. We conclude that deterministic interfaces are no longer sufficient to manage the complexity of modern workflows. The future of software lies not in a fixed set of pre-shipped features, but in dynamic capability sets that grow, adapt, and restructure themselves in real-time to meet the specific intent of the user. This paradigm shift necessitates a fundamental transformation in product development, requiring designers to transcend traditional, linear workflows and evolve into 'System Builders'—architects of the design principles and rules that facilitate this new age of self-evolving software.
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Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Benjamin Hersh
Nels Numan
Jiahao Ren
Xingyue Chen
Robert Timothy Bettridge
Faraz Faruqi
Anthony 'Xiang' Chen
Steve Toh
Google XR, Google (2026)
Preview abstract
While large language models have accelerated software development through "vibe coding", prototyping intelligent Extended Reality (XR) experiences remains inaccessible due to the friction of complex game engines and low-level sensor integration. To bridge this gap, we contribute XR Blocks, an open-source, modular WebXR framework that abstracts spatial computing complexities into high-level, human-centered primitives. Building upon this foundation, we present Vibe Coding XR, an end-to-end rapid prototyping workflow that leverages LLMs to translate natural language intent directly into functional XR software. Using a web-based interface, creators can transform high-level prompts (e.g., "create a dandelion that reacts to hand") into interactive WebXR applications in under a minute. We provide a preliminary technical evaluation on a pilot dataset (VCXR60) alongside diverse application scenarios highlighting mixed-reality realism, multi-modal interaction, and generative AI integrations. By democratizing spatial software creation, this work empowers practitioners to bypass low-level hurdles and rapidly move from "idea to reality." Code and live demos are available at https://xrblocks.github.io/gem and https://github.com/google/xrblocks.
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Approximate vs Precise: An experiment in what impacts user choice when apps request location access
Jessica Johnson
Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26), April 13–17, 2026, Barcelona, Spain (2026)
Preview abstract
User location data is highly sensitive, yet commonly requested by mobile apps for both core functionality and monetization. To improve user privacy, the major mobile platforms, Android and iOS, made changes so that when apps request precise location access, users can choose to share only their approximate location. However, the platforms have diverging interfaces: Android offers a side-by-side choice and iOS offers a corner toggle. This study evaluates which factors impact users’ choices when apps request location access via a randomized controlled experiment with 2579 US Android users. We tested the impact of app type, whether a reason for the request was provided, and the quality and content of the reason, including monetization. We do not find the reasons have an effect. Instead, we find users’ choices are impacted by app type and user demographics. We find that when users are given a side-by-side choice to allow approximate versus precise location access, they make reasonable choices. Of users who allowed access, the vast majority (90.7%) chose precise for a rideshare app versus the majority (71.3%) chose approximate for a local news app. Concerningly, the majority also allowed location access to a wallpaper app, and older users were significantly more likely to allow apps precise location access. We conclude by discussing implications for app platforms and future work.
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Who is At-Risk? Surveying the Prevalence of Risk Factors and Tech-Facilitated Attacks in the United States
Sharon Heung
Claire Weizenegger
Mo Houtti
Tara Matthews
Ashley Walker
2026
Preview abstract
A growing body of qualitative research has identified contextual risk factors that elevate people’s chances of experiencing digital-safety attacks. However, the lack of quantitative data on the population level distribution of these risk factors prevents policymakers and tech companies from developing targeted, evidence-based interventions to improve digital safety. To address this gap, we surveyed 5,001 adults in the United States to analyze: (1) the frequency of and relationship between digital-safety attacks (e.g., scams, harassment, account hacking), and (2) how these attacks align with 10 contextual risk factors. Nearly half of our respondents identify as resource constrained, which significantly correlates with higher likelihood of experiencing four common attacks. We also present qualitative insights to expand our understanding of the factors beyond the existing literature (e.g., “prominence” included high-visibility roles in local communities). This study provides the first large-scale quantitative analysis correlating digital-safety attacks with contextual risk factors and demographics.
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InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs
Jing Jin
Xiuxiu Yuan
Jun Jiang
Jingtao Zhou
Yiyi Huang
Zheng Xu
Kristen Wright
Jason Mayes
Mark Sherwood
Johnny Lee
Alex Olwal
Ram Iyengar
Na Li
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI), ACM, pp. 23
Preview abstract
Visual programming has the potential of providing novice programmers with a low-code experience to build customized processing pipelines. Existing systems typically require users to build pipelines from scratch, implying that novice users are expected to set up and link appropriate nodes from a blank workspace. In this paper, we introduce InstructPipe, an AI assistant for prototyping machine learning (ML) pipelines with text instructions. We contribute two large language model (LLM) modules and a code interpreter as part of our framework. The LLM modules generate pseudocode for a target pipeline, and the interpreter renders the pipeline in the node-graph editor for further human-AI collaboration. Both technical and user evaluation (N=16) shows that InstructPipe empowers users to streamline their ML pipeline workflow, reduce their learning curve, and leverage open-ended commands to spark innovative ideas.
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Beyond Touchscreens: Dynamic and Multimodal Interaction Needs
Melissa Barnhart Wantland
Mai Kobori
Universal Access in Human-Computer Interaction, Springer-Verlag (2025)
Preview abstract
Today’s smartphone interactions are typically designed with one primary preset, accompanied by customization settings that can be manually adjusted. To promote the creation of contextually aware experiences, researchers have highlighted the factors that influence mobile device usage in the ability-based design framework. This paper expands upon existing frameworks and contributes to an empirical understanding of smartphone accessibility. Through a 10-day longitudinal diary study and video interview with 24 individuals who do and do not identify as having a disability, the research also illustrates the reactions of reattempt, adaptation, and avoidance, which were used in response to a lack of smartphone accessibility. Despite experiencing scenarios where accessibility settings could be leveraged, 20 out of 24 participants did not use accessibility settings on their smartphone. A total of 12 out of 24 participants tried accessibility settings on their smartphones, however identifying accessibility was not for them. This work highlights the need to shift current design practices to better serve the accessibility community.
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H2E: Hand, Head, Eye: A Multimodal Cascade of Natural Inputs
Khushman Patel
Hans Gellersen
Ken Pfeuffer
IEEE VR (2025)
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Eye-based interaction techniques for extended reality, such as gaze and pinch, are simple to use however suffer from input precision issues. We present H2E, a fine and coarse-grained pointing technique that cascades Hand, Head, and Eye inputs. As users initiate a pinch gesture, a cursor appears at the gaze point that can be dragged by head pointing before pinch confirmation. This has the potential advantage that it can add a precision component without changing the semantics of the technique. In this paper, we describe the design and implementation of the technique. Furthermore, we present an evaluation of our method in a Fitts-based user study, exploring the speed-accuracy trade-offs against a gaze and pinch interaction baseline.
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Online-EYE: Multimodal Implicit Eye Tracking Calibration for XR
Baosheng James Hou
Lucy Abramyan
Prasanthi Gurumurthy
Khushman Patel
Haley Adams
Andrea Colaco
Ken Pfeuffer
Hans Gellersen
Karan Ahuja
2025
Preview abstract
Unlike other inputs for VR that work out of the box, eye tracking typically requires custom calibration per user or session. We present a multimodal inputs approach for implicit calibration of eye tracker in VR, leveraging UI interaction for continuous, background calibration. Our method analyzes gaze data alongside controller interaction with UI elements, and employing ML techniques it continuously refines the calibration matrix without interrupting users from their current tasks. Potentially eliminating the need for explicit calibration. We demonstrate the accuracy and effectiveness of this implicit approach across various tasks and real time applications achieving comparable eye tracking accuracy to native, explicit calibration.
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Improving simulation-based origin-destination demand calibration using sample segment counts data
Arwa Alanqary
Yechen Li
The 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII), Okinawa, Japan (2025)
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This paper introduces a novel approach to demand estimation that utilizes partial observations of segment-level track counts. Building on established simulation-based demand estimation methods, we present a modified formulation that integrates sample track counts as a regularization term. This approach effectively addresses the underdetermination challenge in demand estimation, moving beyond the conventional reliance on a prior OD matrix. The proposed formulation aims to preserve the distribution of the observed track counts while optimizing the demand to align with observed path-level travel times. We tested this approach on Seattle's highway network with various congestion levels. Our findings reveal significant enhancements in the solution quality, particularly in accurately recovering ground truth demand patterns at both the OD and segment levels.
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Participatory AI Considerations for Advancing Racial Health Equity
Jatin Alla
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI) (2025)
PAIGE: Examining Student Learning Outcomes and Experiences with Personalized AI-Generated Podcasts
Tiffany Do
Usama Bin Shafqat
Elsie Ling
Νikhil Sarda
2025
Preview abstract
Generative AI is revolutionizing content creation and holds promise for real-time, personalized educational experiences. We investigated the effectiveness of converting textbook chapters into AI-generated podcasts and explored the impact of personalizing these podcasts
for individual learner profiles. We conducted a 3x3 user study with 180 college students in the United States, comparing traditional textbook reading with both generalized and personalized AI-generated podcasts across three textbook subjects. The personalized podcasts were tailored to students’ majors, interests, and learning styles. Our findings show that students found the AI-generated podcast format to be more enjoyable than textbooks and that personalized podcasts led to significantly improved learning outcomes, although this was subject-specific. These results highlight that AI-generated podcasts can offer an engaging and effective modality
transformation of textbook material, with personalization enhancing content relevance. We conclude with design recommendations for leveraging AI in education, informed by student feedback.
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