arXiv subjects

Andrés Monroy-Hernández

Publications and source records attributed to Andrés Monroy-Hernández.

At least 19 recordsLinked to original sources

arXiv2026

"Okay, I've Actually Softened My Take on This": How People in Decentralized Social Media Reason about the Appropriateness of Generative AI

Generative AI (GenAI) is increasingly integrated into social media, raising questions about whether, where, and how it belongs. In decentralized social media (DSM), these decisions are distributed across users, developers, moderators, and administrators, making GenAI a collective governance challenge. At the same time, public discourse often flattens arguments to broad pro- or anti-AI positions that offer little insight into what people actually find (in)appropriate and why. Through 20 semi-structured interviews with people from Mastodon and Bluesky, structured around seven GenAI scenarios, we examine how people reason about GenAI's appropriateness in DSM. We find that participants drew conditional boundaries around particular GenAI configurations through distinct, salient, and weighted considerations spanning technology, integration, and use. We conceptualize this as boundary drawing and show how making such boundaries visible can support more grounded design, policy, and collective deliberation around GenAI in DSM.

cs.HC↗

arXiv2026

Empowering Children to Create AI-Enabled Augmented Reality Experiences

Despite their potential to enhance children's learning experiences, AI-enabled AR technologies are predominantly used in ways that position children as consumers rather than creators. We introduce Capybara, an AR-based and AI-powered visual programming environment that empowers children to create, customize, and program 3D characters overlaid onto the physical world. Capybara enables children to create virtual characters and accessories using text-to-3D generative AI models, and to animate these characters through auto-rigging and body tracking. In addition, our system employs vision-based AI models to recognize physical objects, allowing children to program interactive behaviors between virtual characters and their physical surroundings. We demonstrate the expressiveness of Capybara through a set of novel AR experiences. We conducted user studies with 20 children in the United States and Argentina. Our findings suggest that Capybara can empower children to harness AI in authoring personalized and engaging AR experiences that seamlessly bridge the virtual and physical worlds.

cs.HC↗

arXiv2026

How Children Collaborate within Programmable AR Environments with Co-Located Collaborative Features

Programmable augmented reality (AR) environments are emerging as a promising way to support children's creative learning through embodied interaction with digital characters and physical space. At the same time, AR systems are increasingly capable of supporting co-located collaborative experiences. However, little is known about how children collaborate within programmable AR environments offering co-located collaborative features. In response, we extended Capybara, an existing programmable AR application for children, with co-located collaborative features supporting shared visibility and interaction across devices. We then conducted workshops with 9 children to examine whether and how collaboration emerges during use. Across our workshops, collaboration was often lightweight and implicit, emerging through three complementary forms: parallel play with social awareness, iterative remixing, and spontaneous peer support. Together, our findings provide insights for designing future child-centered programmable AR systems that better support co-located collaborative experiences.

cs.HC↗

arXiv2026

How can LLMs Support Policy Researchers? Evaluating an LLM-Assisted Workflow for Large-Scale Unstructured Data

Policy researchers need scalable ways to surface public views, yet they often rely on interviews, listening sessions, and surveys-analyzed thematically-that are slow, expensive, and limited in scale and diversity. LLMs offer new possibilities for thematic analysis of unstructured text, yet we know little about how LLM-assisted workflows perform for policy research. Building on a workflow for LLM-assisted thematic analysis of online forums, we conduct a study with 11 policy researchers, who use an early prototype and see it as a quick, rough-and-ready input to their research. We then extend and scale the workflow to analyze millions of Reddit posts and 1,058 chatbot-led interview transcripts on a policy-relevant topic, treating these sources as rich and scalable data for policy discourse. We compare the synthesized themes to those from authoritative policy reports, identify points of alignment and divergence, and discuss what this implies for policy researchers adopting LLM-assisted workflows.

cs.HC↗

arXiv2026

OpenCourier: an Open Protocol for Building a Decentralized Ecosystem of Community-owned Delivery Platforms

In this vision paper, we outline a blueprint for a decentralized network for the delivery industry, powered by an open protocol. By presenting the network's key components and layers, alongside hypothetical scenarios, we illustrate how the network and the protocol may function in practice. Through this decentralized approach, we aim to address three major issues that mark the current platform-based delivery economy: power imbalances between the platform and workers, information asymmetries caused by opaque decision-making, and value misalignments. Our goal is to provoke dialogue and inspire future work toward more equitable, transparent, and worker-centered futures in the delivery industry, the broader gig economy, and related domains.

cs.HC↗

arXiv2026

AnchorNote: Exploring Speech-Driven Spatial Externalization for Co-Located Collaboration in Augmented Reality

Sticky notes remain a durable collaborative medium because they support rapid idea externalization, rearrangement, and coordination of group attention through spatial organization while being low-friction and lightweight. Recent AR systems suggest new ways to externalize ideas in shared physical space, including spatial annotations and digital workspaces. We introduce AnchorNote, a co-located AR system that lets collaborators intentionally capture spoken ideas as spatially anchored sticky notes via live transcription and LLM summarization. We evaluated AnchorNote in a two-phase iterative study with 20 participants completing a brainstorming and thematic grouping task to examine how speech-driven, spatially persistent capture shapes idea externalization in collaboration. We found that AnchorNote reduced writing effort but reshaped collaboration by introducing new coordination costs and shifting how participants formulated, timed, and organized ideas. We use AnchorNote as an exploratory probe to study how speech-driven, spatial externalization in AR restructures collaborative cognition and coordination, and to derive design implications for future co-located AR collaboration tools.

cs.HC↗

arXiv2026

Bonsai: Intentional and Personalized Social Media Feeds

Social media feeds use predictive models to maximize engagement, often misaligning how people consume content with how they wish to. We introduce Bonsai, a system that enables people to build personalized and intentional feeds. Bonsai implements a platform-agnostic framework comprising Planning, Sourcing, Curating, and Ranking modules. This framework allows users to express their intent in natural language and exert fine-grained control over a procedurally transparent feed creation process. We evaluated the system with 15 Bluesky users in a two-phase, multi-week study. We find that participants successfully used our system to discover new content, filter out irrelevant or toxic posts, and disentangle engagement from intent, but curating intentional feeds required more effort than they are used to. Simultaneously, users sought system transparency mechanisms to effectively use (and trust) intentional, personalized feeds. Overall, our work highlights intentional feedbuilding as a viable path beyond engagement-based optimization.

cs.HC↗

arXiv2026

Governing Together: Toward Infrastructure for Community-Run Social Media

Decentralizing the governance of social computing systems to communities promises to empower them to make independent decisions, with nuance and in accordance with their values. Yet, communities do not govern in isolation. Many problems communities face are common, or move across their boundaries. We therefore propose designing for "inter-community governance:" mechanisms that support relationships and interactions between communities to coordinate on governance issues. Drawing from workshops with 24 individuals on decentralized, community-run social media, we present six challenges in designing for inter-community governance surfaced through ideas proposed in workshops. Together, these ideas come together as an ecosystem of resources, infrastructures, and tools that highlight three key principles for designing for inter-community governance: modularity, forkability, and polycentricity. We end with a discussion of how the ideas proposed in workshops might be implemented in future work aiming to support community governance in social computing systems broadly.

cs.HC↗

arXiv2025

Hapster: Using Apple Watch Haptics to Enable Live Low-Friction Student Feedback in the Physical Classroom

The benefits of student response systems (SRSs) for in-person lectures are well-researched. However, all current SRSs only rely on a visual interface to relay information to the instructor. We describe the design and evaluation of Hapster, a prototype system that uses an Apple Watch to deliver live, aggregated student feedback to the instructor via both visual and vibro-tactile modalities. We evaluated this system with 6 instructors and 155 students at a U.S. university. Participants reported that the system was effective at delivering live student feedback and facilitating better engagement from both the instructor and the students. However, instructors also noted several challenges with differentiating and perceiving the haptic sequences while lecturing. We conclude by discussing the tradeoff between system flexibility and abuse potential while identifying opportunities for further research regarding accessibility, content moderation, and additional interaction modalities. Our results suggest that haptics can be used as an effective live feedback mechanism for instructors in the physical classroom.

cs.HC↗

arXiv2025

Breaking the Plane: Exploring Real-Time Visualization of 3D Surfaces in Augmented Reality with Handwritten Input

We introduce Breaking the Plane, an augmented reality (AR) application built for AR headsets that enables users to visualize 3D mathematical functions using handwritten input. Researchers have demonstrated overlaying 3D visualizations of mathematical concepts through AR enhances learning motivation and comprehension, and equation parsing makes the authoring of teaching materials more time-efficient for instructors. Previous works have developed AR systems that separately employ equation parsing and 3D mathematical visualizations, but work has yet to be done to combine those features by enabling real-time interactions and dynamic visualizations that help users learn in situ. We explore this by developing an AR system featuring handwritten equation parsing, graph manipulation, and a 3D function plotter. We found that our system significantly surpassed other systems in engagement, achieved comparable ease of use to a popular visualization tool, was considered the most effective in aiding problem-solving, and was highly preferred by participants for future use.

cs.HC↗

arXiv2025

FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers

Rideshare workers experience unpredictable working conditions due to gig work platforms' reliance on opaque AI and algorithmic systems. In response to these challenges, we found that labor organizers want data to help them advocate for legislation to increase the transparency and accountability of these platforms. To address this need, we collaborated with a Colorado-based rideshare union to develop FairFare, a tool that crowdsources and analyzes workers' data to estimate the take rate -- the percentage of the rider price retained by the rideshare platform. We deployed FairFare with our partner organization that collaborated with us in collecting data on 76,000+ trips from 45 drivers over 18 months. During evaluation interviews, organizers reported that FairFare helped influence the bill language and passage of Colorado Senate Bill 24-75, calling for greater transparency and data disclosure of platform operations, and create a national narrative. Finally, we reflect on complexities of translating quantitative data into policy outcomes, nature of community based audits, and design implications for future transparency tools.

cs.HC↗

arXiv2025

Understanding Community-Level Blocklists in Decentralized Social Media

Community-level blocklists are key to content moderation practices in decentralized social media. These blocklists enable moderators to prevent other communities, such as those acting in bad faith, from interacting with their own -- and, if shared publicly, warn others about communities worth blocking. Prior work has examined blocklists in centralized social media, noting their potential for collective moderation outcomes, but has focused on blocklists as individual-level tools. To understand how moderators perceive and utilize community-level blocklists and what additional support they may need, we examine social media communities running Mastodon, an open-source microblogging software built on the ActivityPub protocol. We conducted (1) content analysis of the community-level blocklist ecosystem, and (2) semi-structured interviews with twelve Mastodon moderators. Our content analysis revealed wide variation in blocklist goals, inclusion criteria, and transparency. Interviews showed moderators balance proactive safety, reactive practices, and caution around false positives when using blocklists for moderation. They noted challenges and limitations in current blocklist use, suggesting design improvements like comment receipts, category filters, and collaborative voting. We discuss implications for decentralized content moderation, highlighting trade-offs between openness, safety, and nuance; the complexity of moderator roles; and opportunities for future design.

cs.SI↗

arXiv2025

GPTFootprint: Increasing Consumer Awareness of the Environmental Impacts of LLMs

With the growth of AI, researchers are studying how to mitigate its environmental impact, primarily by proposing policy changes and increasing awareness among developers. However, research on AI end users is limited. Therefore, we introduce GPTFootprint, a browser extension that aims to increase consumer awareness of the significant water and energy consumption of LLMs, and reduce unnecessary LLM usage. GPTFootprint displays a dynamically updating visualization of the resources individual users consume through their ChatGPT queries. After a user reaches a set query limit, a popup prompts them to take a break from ChatGPT. In a week-long user study, we found that GPTFootprint increases people's awareness of environmental impact, but has limited success in decreasing ChatGPT usage. This research demonstrates the potential for individual-level interventions to contribute to the broader goal of sustainable AI usage, and provides insights into the effectiveness of awareness-based behavior modification strategies in the context of LLMs.

cs.HC↗

arXiv2025

Seeing the Politics of Decentralized Social Media Protocols

Calls to decentralize feed-based social media have been driven by concerns about the concentrated power of centralized platforms and their societal impact. In response, numerous decentralized social media protocols have emerged, each interpreting "decentralization" in different ways. We analyze four such protocols -- ActivityPub, AT Protocol, Nostr, and Farcaster -- to develop a novel conceptual framework for understanding how protocols operationalize decentralization. Drawing from protocol documentation, media coverage, and first-hand interviews with protocol developers and experts, we contextualize each protocol's approach within their respective socio-technical goals. Our framework highlights how control over key components is distributed differently across each protocol, shaping who holds power over what kinds of decisions. How components are arranged in relation to one another further impacts how component owners might offset each other's power in shaping social media. We argue that examining protocols as artifacts reveals how values shape infrastructure and power dynamics -- and that with a holistic framework as a guide, we can more effectively evaluate and design decentralized platforms aligned with the social and political futures we envision.

cs.SI↗

arXiv2025

ConversAR: Exploring Embodied LLM-Powered Group Conversations in Augmented Reality for Second Language Learners

Group conversations are valuable for second language (L2) learners as they provide opportunities to practice listening and speaking, exercise complex turn-taking skills, and experience group social dynamics in a target language. However, most existing Augmented Reality (AR)-based conversational learning tools focus on dyadic interactions rather than group dialogues. Although research has shown that AR can help reduce speaking anxiety and create a comfortable space for practicing speaking skills in dyadic scenarios, especially with Large Language Model (LLM)-based conversational agents, the potential for group language practice using these technologies remains largely unexplored. We introduce ConversAR, a gpt-4o powered AR application, that enables L2 learners to practice contextualized group conversations. Our system features two embodied LLM agents with vision-based scene understanding and live captions. In a system evaluation with 10 participants, users reported reduced speaking anxiety and increased learner autonomy compared to perceptions of in-person practice methods with other learners.

cs.HC↗

arXiv2025

PolicyPulse: LLM-Synthesis Tool for Policy Researchers

Public opinion shapes policy, yet capturing it effectively to surface diverse perspectives remains challenging. This paper introduces PolicyPulse, an LLM-powered interactive system that synthesizes public experiences from online community discussions to help policy researchers author memos and briefs, leveraging curated real-world anecdotes. Given a specific topic (e.g., "Climate Change"), PolicyPulse returns an organized list of themes (e.g., "Biodiversity Loss" or "Carbon Pricing"), supporting each theme with relevant quotes from real-life anecdotes. We compared PolicyPulse outputs to authoritative policy reports. Additionally, we asked 11 policy researchers across multiple institutions in the Northeastern U.S to compare using PolicyPulse with their expert approach. We found that PolicyPulse's themes aligned with authoritative reports and helped spark research by analyzing existing data, gathering diverse experiences, revealing unexpected themes, and informing survey or interview design. Participants also highlighted limitations including insufficient demographic context and data verification challenges. Our work demonstrates how AI-powered tools can help influence policy-relevant research and shape policy outcomes.

cs.HC↗

arXiv2025

Redefining Research Crowdsourcing: Incorporating Human Feedback with LLM-Powered Digital Twins

Crowd work platforms like Amazon Mechanical Turk and Prolific are vital for research, yet workers' growing use of generative AI tools poses challenges. Researchers face compromised data validity as AI responses replace authentic human behavior, while workers risk diminished roles as AI automates tasks. To address this, we propose a hybrid framework using digital twins, personalized AI models that emulate workers' behaviors and preferences while keeping humans in the loop. We evaluate our system with an experiment (n=88 crowd workers) and in-depth interviews with crowd workers (n=5) and social science researchers (n=4). Our results suggest that digital twins may enhance productivity and reduce decision fatigue while maintaining response quality. Both researchers and workers emphasized the importance of transparency, ethical data use, and worker agency. By automating repetitive tasks and preserving human engagement for nuanced ones, digital twins may help balance scalability with authenticity.

cs.HC↗

arXiv2025

FareShare: A Tool for Labor Organizers to Estimate Lost Wages and Contest Arbitrary AI and Algorithmic Deactivations

What happens when a rideshare driver is suddenly locked out of the platform connecting them to riders, wages, and daily work? Deactivation-the abrupt removal of gig workers' platform access-typically occurs through arbitrary AI and algorithmic decisions with little explanation or recourse. This represents one of the most severe forms of algorithmic control and often devastates workers' financial stability. Recent U.S. state policies now mandate appeals processes and recovering compensation during the period of wrongful deactivation based on past earnings. Yet, labor organizers still lack effective tools to support these complex, error-prone workflows. We designed FareShare, a computational tool automating lost wage estimation for deactivated drivers, through a 6 month partnership with the State of Washington's largest rideshare labor union. Over the following 3 months, our field deployment of FareShare registered 178 account signups. We observed that the tool could reduce lost wage calculation time by over 95%, eliminate manual data entry errors, and enable legal teams to generate arbitration-ready reports more efficiently. Beyond these gains, the deployment also surfaced important socio-technical challenges around trust, consent, and tool adoption in high-stakes labor contexts.

cs.CY↗