LLM impact on BLV programming

Prashant Chandrasekar; Mariel Couvillion; Ayshwarya Saktheeswaran; Jessica Zeitz

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arXiv · 2504.17018

LLM impact on BLV programming

Prashant Chandrasekar·Mariel Couvillion·Ayshwarya Saktheeswaran·Jessica Zeitz

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Abstract

Large Language Models (LLMs) are rapidly becoming integral to a wide range of tools, tasks, and problem-solving processes, especially in software development. Originally designed for natural language processing tasks such as text generation, LLMs are increasingly being used to assist both professionals and students in writing code. This growing reliance on LLM-based tools is reshaping programming workflows and task execution. In this study, we explore the impact of these technologies on blind and low-vision (BLV) developers. Our review of existing literature indicates that while LLMs help mitigate some of the challenges faced by BLV programmers, they also introduce new forms of inaccessibility. We conducted an evaluation of five popular LLM-powered integrated development environments (IDEs), assessing their performance across a comprehensive set of programming tasks. Our findings highlight several unsupported scenarios, instances of incorrect model output, and notable limitations in interaction support for specific tasks. Through observing BLV developers as they engaged in coding activities, we uncovered key interaction barriers that go beyond model accuracy or code generation quality. This paper outlines the challenges and corresponding opportunities for improving accessibility in the context of generative AI-assisted programming. Addressing these issues can meaningfully enhance the programming experience for BLV developers. As the generative AI revolution continues to unfold, it must also address the unique burdens faced by this community.

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BibTeX RIS

Prashant Chandrasekar, Mariel Couvillion, Ayshwarya Saktheeswaran, Jessica Zeitz. 2025-04-23. LLM impact on BLV programming. https://arxiv.org/abs/2504.17018

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