How AI Is Expanding Accessibility

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Think of a world where a person with visual impairment uses their phone to point at a sign they cannot read. The camera captures it and an AI system reads the text aloud in seconds. The person knows what the sign says without asking anyone. Sounds very helpful, right? Fortunately, AI has actually made this a reality.

Accessibility has always been a problem of friction. Suppose a person with visual impairment wants to read a website. Without assistance, it requires screen readers that work inconsistently or text-to-speech software that sounds robotic. With AI, the experience becomes almost seamless. The website becomes readable with the audio sounding natural. The experience gets closer than ever to what a sighted person experiences.

The best part about AI accessibility is that it is working backward from how most AI development happens. Usually, AI is built first and accessibility is retrofitted later if it is added at all. But accessibility AI is being built specifically to solve access problems. That is a design philosophy where entire purpose is inclusion (Digital Defy ND, 2025).

Nearly 1.3 billion people globally have some form of disability. For most of history, technology has either ignored them or forced them to use workarounds. The workarounds sometimes even work but they are exhausting. A person using a screen reader has to remember keyboard shortcuts that do not come naturally. A person using a keyboard instead of a mouse has to navigate interfaces designed for mouse input. It requires a whole lot of more effort.

AI is, at least, removing some of that effort. Real-time captioning means a deaf person can participate in a video call without a human involved in the middle. Sign language recognition means a deaf person can communicate with a hearing person without them knowing sign language. Text-to-speech means a blind person can access information as quickly as a sighted person. These are not workarounds, but significant accessibility features.

Microsoft's Be My Eyes is a simple example of how far this has come. A person who is blind or has low vision points their phone at something they want to understand. An AI system instantly describes what it sees in detail (TestDevLab, 2025). If the AI is unsure, it can connect to a human volunteer who can provide additional context. But most of the time, the AI is enough.

For people with dyslexia or ADHD, the changes are equally significant. ChatGPT can simplify a complex text into simpler language. It can break down instructions step by step. It can read information aloud in a way that helps their brains process it. The tool is not designed specifically for them but it works as if it were.

Navigation is another area where AI is opening possibilities. Wearable devices combined with advanced computer vision can now guide a person with visual impairment through crowded urban spaces with real-time obstacle detection and audio prompts.

AI accessibility often works better at scale. A sign language interpreter might be expensive and hard to find. An AI sign language recognition system works the same way whether there is one deaf person using it or a million. A human transcriber might take hours to transcribe an hour of audio. An AI transcriber does it in minutes. The economics sound way better when machine learning is involved.

The Myaamia Project is a great example. The Myaamia language had nearly died, with only a handful of fluent speakers remaining. The community wanted to revitalize the language but the tools did not exist. An AI system trained on the existing recordings created interactive learning materials that could teach the language to new speakers. The system did not exactly replace human teachers, but at least it made it possible to have a continuous learning environment available whenever someone wanted to learn. The language is now being spoken again by people who would not have had the opportunity without the AI tools (Dartmouth, 2025).

However, AI accessibility, while significantly improving, is not universal yet. Many of these tools are expensive. Some work primarily for wealthy countries where the infrastructure exists. The digital divide still creates barriers for people without reliable internet or devices (Dartmouth, 2025).

The other concern is that these systems are trained on data and that data reflects patterns. An AI system trained on sign language videos from certain regions might not recognize sign language from other regions. An AI trained on faces from certain demographics might not work as well for others. The tool still opens access but it opens unequally.

Regardless of these concerns, the trajectory here is genuinely hopeful. Accessibility AI is expanding what is possible for millions of people. A person who is blind can navigate the world with less reliance on other people. A person with a disability can participate in work, get education and even consume entertainment on more equal terms. An endangered language can be revitalized by its own community without waiting for academic resources. Once these assistance tools become more universal, millions of more lives will change.