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Exploring AI-Based Support in Speech-Language Therapy for Culturally and Linguistically Diverse Children

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Aaleyah Lewis, Aayushi Dangol, Hyewon Suh, Abbie Olszewski, James Fogarty, Julie A. Kientz. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25),

Speech-language pathologists (SLPs) provide support to children with speech and language difficulties through delivering evaluation, assessment, and interventions. Despite growing research on how Artificial Intelligence (AI) can support SLPs, there is limited research examining how AI can assist SLPs in delivering equitable care to culturally and linguistically diverse (CLD) children with disabilities. Through interviews with 15 SLPs and a two-part survey study with 13 SLPs, we report on SLP challenges in delivering responsive care to CLD children with disabilities (i.e., unrepresentative materials, unreliable translation, insufficient support for language variations), areas for AI-based support, evaluations of how available AI performs in addressing these challenges, and bias assessments of AI-generated materials. We discuss implications of contextually unaware AI, the range of care in AI prompting, tensions and tradeoffs of AI-based support, and honoring diverse representations in AI-generated materials. We offer considerations for SLPs using AI-based tools and general-purpose AI in their practice.

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

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Tobias M. Weinberg, Aaleyah Lewis, Ricardo E. Gonzalez Penuela, Weicong Hong, Jennifer Mankoff, Thijs Roumen. ASSETS 2026.

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.

Capstone Course: Accessibility and Race

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Disabled people live at the intersection of multiple identities that shape their access needs and technology experiences. Designing equitable systems requires preparing future technologists to critically engage with how technologies interact with systems of oppression. We approached a quarter-long capstone course with one central question: Do the processes and expectations of engineering education (in the context of an accessibility capstone) adequately incorporate multiple intersectional perspectives? By using this as a guiding pedagogical framework, we uncovered gaps in standard processes and requirements and developed practical solutions for addressing them. We outline the theoretical foundations informing the course, its structure and implementation, and reflect on the tensions that emerged throughout the course. We offer considerations for educators seeking to meaningfully integrate intersectional accessibility into computing education.

Aaleyah Lewis, Stacy Hsueh, Jennifer Mankoff Integrating Intersectional Frameworks Into an Accessibility Capstone.RESPECT 2026: 244-254 (html)

“I Don’t Trust it, but I Use it”: Navigating Trust, Privacy, and Identity in Disabled People’s Use of Generative AI

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Jazette Johnson, Aaleyah Lewis, Jennifer Mankoff, and Olivia Banner. 2026. “I Don’t Trust it, but I Use it”: Navigating Trust, Privacy, and Identity in Disabled People’s Use of Generative AI. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26), April 13–17

As generative AI (GenAI) is integrated into everyday technologies, it offers new accessibility opportunities and risks for disabled people. However, little is known about how disabled people navigate GenAI in their everyday lives, particularly how trust, privacy, and intersectional identities affect these experiences. We present findings from seven cross-disability focus groups (N=20) that explore how disabled people navigate GenAI. Our findings reveal that while GenAI supports autonomy, efficiency, and communication, it also introduces accessibility taxes and ethical dilemmas. Although participants voiced skepticism, many continued using GenAI out of necessity. Finally, we found identity-based benefits and tensions, in which GenAI preserved and validated intersecting identities, but also misrepresented and erased those identities. We frame these negotiations as a constant balancing act between access and risk, urging research to further examine how “access” is conceptualized. We offer implications for creating GenAI tools that are transparent, trustworthy, and responsive to intersectional identities.

What Do We Mean by “Accessible”

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Lots of people have ideas about what “accessible” means — but they don’t all agree. Maybe we should ask disabled people. We could also learn a lot by asking a wide variety of disabled people, including disabled people who are gender diverse, racially diverse, have multiple disabilities, and have a wide range of disabilities.

We asked 25 disabled people about what accessibility means to them. We learned that it goes beyond typical definitions of addressing an impairment of some kind. We also learned about how people decide what accessibility technologies they want to use. Many people told us that they choose from many possible approaches in each specific situation, weighing all the available options and their priorities in a so-called “consequence calculus”.

Reference: Modeling Accessibility: Characterizing What We Mean by “Accessible” Kelly Avery Mack, Jesse J Martinez, Aaleyah Lewis, Jennifer Mankoff, James Fogarty, Leah Findlater, Heather D. Evans, Cynthia L Bennett, Emma J McDonnell. ASSETS 2025

Autoethnographic Insights from Neurodivergent GAI “Power Users”

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Kate Glazko, JunHyeok Cha, Aaleyah Lewis, Ben Kosa, Brianna Wimer, Andrew Zheng, Yiwei Zheng, and Jennifer Mankoff. 2025. Autoethnographic Insights from Neurodivergent GAI “Power Users”. In CHI Conference on Human Factors in Computing Systems (CHI ’25), April 26-May 1, 2025, Yokohama, Japan. ACM, New York, NY, USA, 20 pages. https://doi.org/10.1145/ 3706598.3713670

Generative AI has become ubiquitous in both daily and professional life, with emerging research demonstrating its potential as a tool for accessibility. Neurodivergent people, often left out by existing accessibility technologies, develop their own ways of navigating normative expectations. GAI offers new opportunities for access, but it is important to understand how neurodivergent “power users”—successful early adopters—engage with it and the challenges they face. Further, we must understand how marginalization and intersectional identities influence their interactions with GAI. Our autoethnography, enhanced by privacy-preserving GAI-based diaries and interviews, reveals the intricacies of using GAI to navigate normative environments and expectations. Our findings demonstrate how GAI can both support and complicate tasks like code-switching, emotional regulation, and accessing information. We show that GAI can help neurodivergent users to reclaim their agency in systems that diminish their autonomy and self-determination. However, challenges such as balancing authentic self-expression with societal conformity, alongside other risks, create barriers to realizing GAI’s full potential for accessibility.

Toward Language Justice

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Aashaka Desai, Rahaf Alharbi, Stacy Hsueh, Richard E. Ladner, and Jennifer Mankoff. 2025. Toward Language Justice: Exploring Multilingual Captioning for Accessibility. In CHI Conference on Human Factors in Computing Systems (CHI ’25), April 26–May 01, 2025, Yokohama, Japan. ACM, New York, NY, USA, 18 pages. https://doi.org/10.1145/3706598.3713622

A growing body of research investigates how to make captioning experiences more accessible and enjoyable to disabled people. However, prior work has focused largely on English captioning, neglecting the majority of people who are multilingual (i.e., understand or express themselves in more than one language). To address this gap, we conducted semi-structured interviews and diary logs with 13 participants who used multilingual captions for accessibility. Our findings highlight the linguistic and cultural dimensions of captioning, detailing how language features (scripts and orthography) and the inclusion/negation of cultural context shape the accessibility of captions. Despite lack of quality and availability, participants emphasized the importance of multilingual captioning to learn a new language, build community, and preserve cultural heritage. Moving toward a future where all ways of communicating are celebrated, we present ways to orient captioning research to a language justice agenda that decenters English and engages with varied levels of fluency.

Race, Disability and Accessibility Technology

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As part of a team of researchers, we have been exploring best practices in incorporating disability justice principles into accessibility research and more generally thinking about how to best incorporate multi-racial, and associated multi-cultural and multi-lingual, perspectives into accessibility work in a deep way. Most of the accessibility research that was published in the past does not mention race, or treats it as a simple label rather than asking how it impacts disability experiences. This eliminates whole areas of need and vital perspectives from the work we do.

This has resulted in two articles. The first, “Working at the Intersection of Race, Disability, and Accessibility” [1] asks how research in accessibility can do a better job of including disabled persons as whole, diverse people, rather than separating disability from a person’s race and ethnicity. In the paper, we present a series of case studies of other researchers’ work exploring positive examples of work that looks more deeply at this intersection and reflect on teaching at the intersection of race, disability, and technology. This paper highlights the value of considering how constructs of race and disability work alongside each other within accessibility research studies, designs of socio-technical systems, and education. Our analysis provides recommendations towards establishing this research direction.

The second, “Race, Disability, and Technology: A Call to Action for Accessibility Researchers” [2] examines what we learned from applying the framework featured in the first. This time, we reflect on our own experience applying the framework and reflect on tensions and considerations that arise while pursuing this work.

[1] Christina N. Harrington, Aashaka Desai, Aaleyah Lewis, Sanika Moharana, Anne Spencer Ross, Jennifer Mankoff: Working at the Intersection of Race, Disability and Accessibility. ASSETS 2023: 26:1-26:18 (pdf)

[2] Aashaka Desai*, Aaleyah Lewis*, Sanika Moharana*, Anne Spencer Ross, Jennifer Mankoff, Christina N. Harrington. Race, Disability, and Technology: A Call to Action for Accessibility Researchers.ACM Trans. Access. Comput. 19(1): 3:1-3:31 (2026) (html)