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Building an accessible application with AI works best when you replace “make it accessible” with tasks a person can actually test. In my work on Unity Access, an editor plugin intended to make Unity more usable for screen-reader users, that meant defining keyboard access, screen-reader announcements, control states, and ways to move through and leave each interface.
I’m Daniel Longmore, Unity Access developer. This is a retrospective on what helped, what went wrong, and how I now scope AI-assisted accessibility work. My account reflects one project and workflow, not a benchmark of coding models or a claim that AI can validate accessibility on its own.
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What Unity Access set out to do
Unity Access is an editor plugin intended to make Unity more usable for people who use screen readers. I built it using the Unity Editor API alongside the NVDA API, testing in Unity with a URP sample project and using NVDA as my screen reader. I wrote Markdown specifications in VS Code and used Codex with GPT-5.6 Sol at medium reasoning effort. Those details describe my workflow at the time of this account, published August 25, 2026; they are not a general recommendation or current model comparison.
The core challenge was translating a broad goal into implementation requirements. “Make it accessible” does not tell an AI what a person should be able to do, what they should hear, or how to tell whether the result works.
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Turn accessibility goals into testable user tasks
For each feature, describe the experience from the user’s point of view. A requirement should make it possible to try the interface with a keyboard and the intended assistive technology, then judge whether the expected result occurred.
- Reach: Can a user navigate to every relevant control with the keyboard?
- Understand: What does NVDA announce for each control, including its purpose and current value or state?
- Operate: Can the user complete the task without a mouse?
- Navigate: Can the user leave the interface and return without losing their place or becoming stranded?
For example, instead of asking an AI to make a menu accessible, specify that a user must be able to navigate this menu using NVDA, hear each item’s name and state, activate an item with the keyboard, and return to the prior context. The exact expected announcements and behavior depend on the interface, so define them for the feature rather than assuming a generic instruction will cover them.
As I put it: “Do not tell AI to “make it accessible”. Tell it exactly what an accessible user must be able to do.”
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Set boundaries before asking for code
I found it useful to put project rules in a root AGENTS.md file. Keep it focused on constraints that apply across changes, such as:
- Which files the AI may edit and which APIs it may use.
- The scope and intended size of a change.
- Accessibility expectations, including keyboard and screen-reader behavior.
- What counts as complete, such as the user tasks or checks that must work.
Then split large features into focused requests. Narrower changes made it easier for me to review what the AI had done and reduced unnecessary refactoring. The instructions do not replace review: they make the intended scope clearer.
Standardize controls and organize the specification
Reuse accessible UI primitives
Before I standardized shared controls, UI behavior varied between features. I found that reusable utilities for buttons, text boxes, and other controls reduced that variation and made improvements easier to carry across the project. When asking an AI to add a feature, direct it to use the existing accessible controls rather than inventing a new variant without a clear reason.
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Keep requirements concise and structured
Related folders helped me navigate the project with NVDA and made it easier to scope command-line work. I also found concise specifications with clear headings easier to review than long, essay-like documents, where important requirements could get buried.
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Writing UI and backend requirements separately helped me revise one area without unintentionally changing the other. This distinction is especially useful when a feature has both user-facing behavior and project or data operations: state what the interface must do, then define the backend responsibilities and constraints independently.
Verify technical requirements before implementation
I once used ChatGPT to help draft Unity project-settings requirements in an area where I did not have much technical familiarity. The specification omitted settings and included outdated API references. Codex implemented those instructions, and the resulting errors sent Unity into Safe Mode and required additional debugging.
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The lesson was not to treat one AI’s output as unquestioned technical truth for another AI to implement. When a requirement depends on framework APIs or settings—especially when you are asking AI to fill a knowledge gap—check it against current official documentation before coding. An apparently precise specification can still be incomplete or stale.
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In my workflow, I tried GPT-5.6 Lunar in place of GPT-5.6 Sol, partly to see whether it could reduce token costs. On a large asset-viewer task, I found Lunar less effective; I had to correct the result and substantially rewrite parts of the viewer. I found it more suitable for smaller, tightly focused tasks.
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That is one dated, project-specific experience, not evidence that one model is generally better. When deciding how to approach a task, consider its complexity, whether the implementation follows current APIs and accessibility requirements, and how much correction it takes. Token price alone does not show the total effort or cost if the output needs substantial rework. As I concluded: “The cheapest model per token is not necessarily the cheapest model for the overall task.”
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Test with people who use the target technology
AI can help implement requirements, but it cannot stand in for feedback from people who depend on accessibility features. Ask screen-reader users what their screen reader announces, what takes too many keystrokes, what is confusing, and what they cannot reach at all. Their feedback can expose a mismatch between the behavior you specified and the experience the interface actually provides.
Start accessibility work early, while the interface is still being shaped. Retrofitting keyboard and screen-reader behavior into a finished design can mean revisiting assumptions that would have been easier to address sooner.
What the later Unity Access update described
In a separate update published August 31, 2026, I described a growing feature set that included an asset viewer for viewing, deleting, moving, and renaming files and folders; object actions such as rename, delete, duplicate, parenting, and prefab creation; a basic console; scene creation; selected project settings; and build-profile controls. The update described installing the plugin by copying its Editor folder into a Unity project’s Assets directory and allowing Unity to compile the scripts.
Those are dated descriptions from my project updates, not independent verification of present release status, compatibility, or repository contents. Check the project’s current documentation and code before relying on those details.
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