arXiv subjects

Ricardo E. Gonzalez Penuela

Publications and source records attributed to Ricardo E. Gonzalez Penuela.

9 recordsLinked to original sources

arXiv2026

Supporting Allyship in Virtual Collaboration with Artificial Intelligence: A Scenario-Based Study

Recent research has revealed that accessibility in virtual collaboration is not only a technical problem but also depends on allyship: the informal, interpersonal practices through which people support others' accessibility needs. Yet little research has examined how artificial intelligence (AI) might support allyship in collaborative accessibility contexts. To address this gap, we conducted a study where 18 participants (10 disabled, 8 non-disabled) reflected on four allyship scenarios with novel AI tools. Participants valued AI designs that they anticipated could help teammates express, interpret, and coordinate around access needs by reducing repeated disclosure and making allyship more actionable. At the same time, participants raised concerns when AI acted without consent, misrepresented users' intentions, or displaced the interpersonal work of allyship. Based on our findings, we introduce allyship-support tools as a class of accessibility technologies, offer design guidelines, and discuss AI in particular as a means of supporting allyship.

cs.HC↗

arXiv2026

Me, Myself, and My Voice: Exploring Cultural and Linguistic Identity in AAC AI-generated Voices

Voice is a central element of identity. We recognize people by their voice, and we uniquely express who we are with it. For people who rely on augmentative and alternative communication (AAC) systems, such as speech-generating devices (SGD), the device's voice becomes an identity marker others associate with them. Yet, it is hard to find a voice that truly aligns with one's identity both linguistically and culturally. Although modern AI-generated voices can reproduce diverse accents and speaking styles, AAC users still lack accessible ways to articulate how they want an identity-aligned voice to sound like. We first conducted a survey of AAC users (across eight countries) to characterize current voice representation, finding that non-binary, transgender, and non-US-born respondents rated their current voice support identity alignment consistently lower than other respondents. To examine how AAC users respond to voices designed to reflect their cultural identity, we built a tool that elicits cultural markers through guided questions and generates personalized voice candidates for participants to hear and reflect on. After participants heard the voices, we interviewed them to examine what it means for a voice to feel culturally representative, how they interpreted voices with cultural connotations, and how these voices shaped their sense of identity and agency. Our findings show that cultural voice alignment runs deeper than accent or language alone; it touches on belonging, self-recognition, and what it means to be heard as who you are.

cs.HC↗

arXiv2026

I, Robot? Exploring Ultra-Personalized AI-Powered AAC; an Autoethnographic Account

Generic AI auto-complete for message composition often fails to capture the nuance of personal identity, requiring editing. While harmless in low-stakes settings, for users of Augmentative and Alternative Communication (AAC) devices, who rely on such systems to communicate, this burden is severe. Intuitively, the need for edits would be lower if language models were personalized to the specific user's communication. While personalization is technically feasible, it raises questions about how such systems affect AAC users' agency, identity, and privacy. We conducted an autoethnographic study in three phases: (1) seven months of collecting all the lead author's AAC communication data, (2) fine-tuning a model on this dataset, and (3) three months of daily use of personalized AI suggestions. We observed that: logging everyday conversations reshaped the author's sense of agency, model training selectively amplified or muted aspects of his identity, and suggestions occasionally resurfaced private details outside their original context. We find that ultra-personalized AAC reshapes communication by continually renegotiating agency, identity, and privacy between user and model. We highlight design directions for building personalized AAC technology that supports expressive, authentic communication.

cs.HC↗

arXiv2026

How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision People

Multimodal large language models (MLLMs) are changing how Blind and Low Vision (BLV) people access visual information. Unlike traditional visual interpretation tools that only provide descriptions, MLLM-enabled applications offer conversational assistance, where users can ask questions to obtain goal-relevant details. However, evidence about their performance in the real-world and implications for BLV people's daily lives remains limited. To address this, we conducted a two-week diary study, where we captured 20 BLV participants' use of an MLLM-enabled visual interpretation application. Although participants rated the visual interpretations of the application as "trustworthy" (mean=3.76 out of 5, max=extremely trustworthy) and "somewhat satisfying" (mean=4.13 out of 5, max=very satisfying), the AI often produced incorrect answers (22.2%) or abstained (10.8%) from responding to users' requests. Our findings show that while MLLMs can improve visual interpretations' descriptive accuracy, supporting everyday use also depends on the "visual assistant" skill: behaviors for providing goal-directed, reliable assistance. We conclude by proposing the "visual assistant" skill and guidelines to help MLLM-enabled visual interpretation applications better support BLV people's access to visual information.

cs.HC↗

arXiv2025

One Does Not Simply 'Mm-hmm': Exploring Backchanneling in the AAC Micro-Culture

Backchanneling (e.g., "uh-huh", "hmm", a simple nod) encompasses a big part of everyday communication; it is how we negotiate the turn to speak, it signals our engagement, and shapes the flow of our conversations. For people with speech and motor impairments, backchanneling is limited to a reduced set of modalities, and their Augmentative and Alternative Communication (AAC) technology requires visual attention, making it harder to observe non-verbal cues of conversation partners. We explore how users of AAC technology approach backchanneling and create their own unique channels and communication culture. We conducted a workshop with 4 AAC users to understand the unique characteristics of backchanneling in AAC. We explored how backchanneling changes when pairs of AAC users communicate vs when an AAC user communicates with a non-AAC user. We contextualize these findings through four in-depth interviews with speech-language pathologists (SLPs). We conclude with a discussion about backchanneling as a micro-cultural practice, rethinking embodiment and mediation in AAC technology, and providing design recommendations for timely multi-modal backchanneling while respecting different communication cultures.

cs.HC↗

arXiv2025

Towards Understanding the Use of MLLM-Enabled Applications for Visual Interpretation by Blind and Low Vision People

Blind and Low Vision (BLV) people have adopted AI-powered visual interpretation applications to address their daily needs. While these applications have been helpful, prior work has found that users remain unsatisfied by their frequent errors. Recently, multimodal large language models (MLLMs) have been integrated into visual interpretation applications, and they show promise for more descriptive visual interpretations. However, it is still unknown how this advancement has changed people's use of these applications. To address this gap, we conducted a two-week diary study in which 20 BLV people used an MLLM-enabled visual interpretation application we developed, and we collected 553 entries. In this paper, we report a preliminary analysis of 60 diary entries from 6 participants. We found that participants considered the application's visual interpretations trustworthy (mean 3.75 out of 5) and satisfying (mean 4.15 out of 5). Moreover, participants trusted our application in high-stakes scenarios, such as receiving medical dosage advice. We discuss our plan to complete our analysis to inform the design of future MLLM-enabled visual interpretation systems.

cs.HC↗

arXiv2025

Why So Serious? Exploring Timely Humorous Comments in AAC Through AI-Powered Interfaces

People with disabilities that affect their speech may use speech-generating devices (SGD), commonly referred to as Augmentative and Alternative Communication (AAC) technology. This technology enables practical conversation; however, delivering expressive and timely comments remains challenging. This paper explores how to extend AAC technology to support a subset of humorous expressions: delivering timely humorous comments -- witty remarks -- through AI-powered interfaces. To understand the role of humor in AAC and the challenges and experiences of delivering humor with AAC, we conducted seven qualitative interviews with AAC users. Based on these insights and the lead author's firsthand experience as an AAC user, we designed four AI-powered interfaces to assist in delivering well-timed humorous comments during ongoing conversations. Our user study with five AAC users found that when timing is critical (e.g., delivering a humorous comment), AAC users are willing to trade agency for efficiency contrasting prior research where they hesitated to delegate decision-making to AI. We conclude by discussing the trade-off between agency and efficiency in AI-powered interfaces, how AI can shape user intentions, and offer design recommendations for AI-powered AAC interfaces.

cs.HC↗

arXiv2024

Accessible Nonverbal Cues to Support Conversations in VR for Blind and Low Vision People

Social VR has increased in popularity due to its affordances for rich, embodied, and nonverbal communication. However, nonverbal communication remains inaccessible for blind and low vision people in social VR. We designed accessible cues with audio and haptics to represent three nonverbal behaviors: eye contact, head shaking, and head nodding. We evaluated these cues in real-time conversation tasks where 16 blind and low vision participants conversed with two other users in VR. We found that the cues were effective in supporting conversations in VR. Participants had statistically significantly higher scores for accuracy and confidence in detecting attention during conversations with the cues than without. We also found that participants had a range of preferences and uses for the cues, such as learning social norms. We present design implications for handling additional cues in the future, such as the challenges of incorporating AI. Through this work, we take a step towards making interpersonal embodied interactions in VR fully accessible for blind and low vision people.

cs.HC↗

arXiv2022

Uncovering Visually Impaired Gamers' Preferences for Spatial Awareness Tools Within Video Games

Sighted players gain spatial awareness within video games through sight and spatial awareness tools (SATs) such as minimaps. Visually impaired players (VIPs), however, must often rely heavily on SATs to gain spatial awareness, especially in complex environments where using rich ambient sound design alone may be insufficient. Researchers have developed many SATs for facilitating spatial awareness within VIPs. Yet this abundance disguises a gap in our understanding about how exactly these approaches assist VIPs in gaining spatial awareness and what their relative merits and limitations are. To address this, we investigate four leading approaches to facilitating spatial awareness for VIPs within a 3D video game context. Our findings uncover new insights into SATs for VIPs within video games, including that VIPs value position and orientation information the most from an SAT; that none of the approaches we investigated convey position and orientation effectively; and that VIPs highly value the ability to customize SATs.

cs.HC↗