mlsanityData to 14 Oct 2026

The Impact of Generative AI Coding Assistants on Developers Who Are Visually Impaired

arXiv 2503.16491 ↗First posted 10 Mar 2025cs.HC cs.AI cs.CYcited by 7

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Abstract (arXiv)

The rapid adoption of generative AI in software development has impacted the industry, yet its effects on developers with visual impairments remain largely unexplored. To address this gap, we used an Activity Theory framework to examine how developers with visual impairments interact with AI coding assistants. For this purpose, we conducted a study where developers who are visually impaired completed a series of programming tasks using a generative AI coding assistant. We uncovered that, while participants found the AI assistant beneficial and reported significant advantages, they also highlighted accessibility challenges. Specifically, the AI coding assistant often exacerbated existing accessibility barriers and introduced new challenges. For example, it overwhelmed users with an excessive number of suggestions, leading developers who are visually impaired to express a desire for ``AI timeouts.'' Additionally, the generative AI coding assistant made it more difficult for developers to switch contexts between the AI-generated content and their own code. Despite these challenges, participants were optimistic about the potential of AI coding assistants to transform the coding experience for developers with visual impairments. Our findings emphasize the need to apply activity-centered design principles to generative AI assistants, ensuring they better align with user behaviors and address specific accessibility needs. This approach can enable the assistants to provide more intuitive, inclusive, and effective experiences, while also contributing to the broader goal of enhancing accessibility in software development.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 21 of its 112 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

21 of 112 references.

References · 21

  1. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  2. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274
  3. 2022GitHub Copilot AI pair programmer: Asset or Liability?cited by 152
  4. 2022Productivity Assessment of Neural Code Completioncited by 98
  5. 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
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  10. 2023Investigating and Designing for Trust in AI-powered Code Generation Toolscited by 34
  11. 2023Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Reviewcited by 32
  12. 2023Navigating the Complexity of Generative AI Adoption in Software Engineeringcited by 32
9 more
  1. 2024How Beginning Programmers and Code LLMs (Mis)read Each Othercited by 31
  2. 2023Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programmingcited by 20
  3. 2022Rich Screen Reader Experiences for Accessible Data Visualizationcited by 12
  4. 2022Datavoidant: An AI System for Addressing Political Data Voids on Social Mediacited by 6
  5. 2023The Transformative Influence of LLMs on Software Development & Developer Productivitycited by 5
  6. 2024Chart4Blind: An Intelligent Interface for Chart Accessibility Conversioncited by 5
  7. 2022The Global Care Ecosystems of 3D Printed Assistive Devicescited by 3
  8. 2024Understanding the Career Mobility of Blind and Low Vision Software Professionalscited by 1
  9. 2024Accessibility evaluation of major assistive mobile applications available for the visually impairedcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.