Hello,

I am a fifth-year PhD student co-advised by James Fogarty and Jennifer Mankoff in Computer Science & Engineering at the University of Washington. I am grateful to be an ARCS Foundation Scholar and a GEM Fellow. My research interest is in Accessibility and Human-Computer Interaction (HCI), particularly developing more equitable and accessible technology to empower and support people with disabilities. Within this, I am interested in examining the intersection of race, langauge, and disability as it pertains to the engagement with AI technology, particularly for people of color with disabilities and multilingual people with disabilities who have diverse communication patterns. In exploring this research, I hope to advance the design and development of inclusive AI technologies that honor intersectionality.

I recieved my Bachelor's degree from the University of Maryland, Baltimore County (UMBC) in May 2021 where I majored in Computer Science and minored in Psychology. At UMBC, I was a McNair Scholar , LSAMP Scholar and a Center for Women in Technology (CWIT) Affiliate.

Research Interests

  • Accessibility
  • Human-Computer Interaction
  • Responsible AI
  • Inclusive Design

Selected Projects

Digital Code-Switching and Masking in AI Use for Multilingual and Multicultural People with Disabilities

This project explores how multilingual and multicultural people with disabilities engage in code-switching and masking in the context of AI use; how AI technologies support, facilitate, undermine, and necessitate these practices; the challenges and advantages users encounter in these practices; and users' desires for AI's role in shaping context-sensitive identity expression. In this, we aim to understand how AI might be reimagined and re-designed to center user agency and self-determination around such practices across diverse sociotechnical environments.

Identity, Trust, and Privacy in Disabled People’s Use of Generative AI

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.

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

Speech-language pathologists (SLPs) are professionals who 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 SLP perceptions of challenges in delivering responsive care to CLD children with disabilities (i.e., insufficient representative materials, inaccurate and inefficient translation, and 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 the range of care in AI-prompting, tensions and tradeoffs of AI-based support, and honoring diverse representations in AI-generated materials. Finally, we offer considerations for AI-based support in speech-language pathology.

Inaccessible and Deceptive: Examining Experiences of Deceptive Design with People Who Use Visual Accessibility Technology

Deceptive design patterns are interface designs that manipulate people into actions to which they would otherwise object. Despite growing research on deceptive design, limited research examines its interplay with accessibility and visual accessibility technology (e.g.,~screen readers, screen magnification, braille displays). We present an interview and subsequent diary study with 16 people who use visual accessibility technology to better understand their experiences with accessibility and deceptive design. We report participant experiences with six deceptive design patterns, including both designs that are intentionally deceptive and designs where participants describe accessibility barriers manifesting as deceptive, together with direct and indirect consequences of deceptive patterns. We discuss intent versus impact in accessibility and deceptive design, how access barriers exacerbate harms of deceptive design, and impacts of deceptive design from a perspective of consequence-based accessibility. We propose that accessibility tools could help address deceptive design patterns by offering higher-level feedback to well-intentioned designers.


Publications and Presentations

Race, Disability, and Technology: A Call to Action for Accessibility Researchers

Aashaka Desai*, Aaleyah Lewis*, Sanika Moharana*, Anne Spencer Ross, Jennifer Mankoff, Christina N. Harrington. Accepted in ACM Transactions on Accessible Computing (TACCESS 2025).

Modeling Accessibility: Characterizing What We Mean by "Accessible"

Kelly Avery Mack, Jesse J Martinez, Aaleyah Lewis, Jennifer Mankoff, James Fogarty, Leah Findlater, Dr. Heather D. Evans, Cynthia L Bennett, Emma J McDonnell. Accepted at ASSETS 2025.

Inaccessible and Deceptive: Examining Experiences of Deceptive Design with People Who Use Visual Accessibility Technology

Aaleyah Lewis, Jesse J. Martinez, Maitraye Das, James Fogarty. CHI 2025 Conference on Human Factors in Computing Systems.[PDF]

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

Aaleyah Lewis, Aayushi Dangol, Hyewon Suh, Abbie Olszewski, James Fogarty, Julie A. Kientz. CHI 2025 Conference on Human Factors in Computing Systems. [PDF]

Autoethnographic Insights from Neurodivergent GAI "Power Users"

Kate Glazko*, JunHyeok Cha*, Aaleyah Lewis, Ben Kosa, Brianna Wimer, Andrew Zheng, Roy Zheng, Jennifer Mankoff. CHI 2025 Conference on Human Factors in Computing Systems.

"I want to think like an SLP": A Design Exploration of AI-Supported Home Practice in Speech Therapy

Aayushi Dangol, Aaleyah Lewis, Hyewon Suh, Xuesi Hong, Hedda Meadan, James Fogarty, Julie A. Kientz. CHI 2025 Conference on Human Factors in Computing Systems.

Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology

Jay L. Cunningham, Adinawa Adjagbodjou, Jeffrey Basoah, Jainaba Jawara, Kowe Kadoma, Aaleyah Lewis. AIES 2025.

Working at the Intersection of Race, Disability, and Accessibility

Christina N. Harrington, Aashaka Desai, Aaleyah Lewis, Sanika Moharana, Anne Spencer Ross, Jennifer Mankoff. ASSETS 2023: SIGACCESS Conference on Computers and Accessibility. [PDF]

Towards Intersectional CUI Design Approaches for African American English Speakers with Dysfuencies

Aaleyah Lewis, Orevaoghene Ahia, Jay L. Cunningham, James Fogarty. [Workshop Paper] ACM CHI 2023. CUI@CHI: Inclusive Design of CUIs Across Modalities and Mobilities. [PDF]

Using Fiber Arts and Sonification to Improve Data Accessibility of Maker Spaces

Aashaka Desai, Venkatesh Potluri, Aaleyah Lewis*, Daniel Campos Zamora*, Jayne Everson*, Jennifer Mankoff, Richard E. Ladner. [Workshop Paper] ACM CHI 2022. Reimagining Systems for Learning Hands-On Creative and Maker Skills. [PDF]

Stanford Ocean Acidification VR Experience

Aaleyah Lewis, Anna Queiroz, Jeremy Bailenson.
Stanford University, 2020

Conflict Mediation at Scale: Leveraging Big Data to Mediate Online Conflicts

Aaleyah Lewis, Ru Zhao, Susan Fussell.
Cornell University, 2019

* Indicates authors contributed equally to this work and are considered first author.

Highlights

2024  I moderated a talk with Patty Berne on Disability Justice: Centering Intersectionality and Liberation

2023  Speaker at the Paul G. Allen School of CSE Accessibility Colloquium

2023  Awarded UW CREATE's Race, Disability & Technology Grant

2023  Attended the CRA-WP Grad Cohort Workshop for Inclusion, Diversity, Equity, Accessibility, and Leadership Skills (IDEALS)

2022  Guest speaker at the 2022 LSAMP Conference

2020, 2018  Speaker at the annual LSAMP Summer Bridging Conference

2019  Speaker at BlackcomputeHER Conference

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