Use AI to Review Content Accessibility: A WCAG Workflow

Use AI to review content accessibility by giving it the actual page text, heading structure, links and form messages. Ask for specific wording and structure issues with proposed revisions. Then check the page with appropriate manual and technical methods: an AI text review cannot establish that an interface is accessible or that it conforms to WCAG.

This workflow focuses on content a writer can improve, including unclear instructions, vague links and confusing errors. It complements design, engineering and user testing rather than replacing them.

Give the reviewer structure, not just prose#

Paste the page title and headings with their levels. Include link labels with their destinations and form fields with their labels, instructions and error messages. For images, describe their purpose in the page. The same picture can be informative in one context and decorative in another.

If you provide only a screenshot, the model may miss the semantic structure exposed to assistive technology. If you provide only HTML, it may not know whether controls are visible, whether focus is usable or how an error is announced. State which evidence is available and which checks remain.

Use a focused content checklist#

W3C's writing guidance covers titles, headings, links, image alternatives and instructions. Use those topics as a starting point, then check the applicable requirements for your product and context.

Do not ask AI to add an accessibility attribute to every element automatically. The right implementation depends on semantics and actual interaction behavior. Unnecessary or incorrect attributes can create additional confusion.

Copy this content-review prompt#

Review this page's content for accessibility and clarity.
Audience and task: [details]. Page title and heading levels: [paste].
Links with destinations: [paste]. Forms, instructions and errors: [paste].
Image purposes and current alternatives: [paste].
For each issue, quote the exact text, explain the reader difficulty and
propose a specific revision. Separate wording issues from semantic or
interaction checks requiring a developer or manual testing.
Do not claim WCAG conformance or infer unseen interface behavior.
Identify the relevant official W3C guidance where established.

Ask for a short prioritized list. A page may have many stylistic opportunities, but a missing prerequisite that prevents task completion deserves attention before minor wording preferences.

Review meaningful links and instructions#

Consider a fictional upload form that says “Use the correct file.” A clearer instruction might say “Upload a CSV file with columns Email and Team,” if those are the actual accepted format and required fields. The revision should come from the product rules, not from an assumption that CSV is supported.

A link labeled “Read more” may be understandable in its immediate context, but repeated ambiguous links can make navigation harder. Use labels that identify the destination or task. Check the whole sentence so the label still reads naturally.

Handle image alternatives in context#

For an informative image, explain what matters to the task. For a functional image, communicate the action. Decorative images may need an empty alternative rather than a verbose visual description. W3C's image decision tree helps distinguish these cases.

For charts, a short alt description may need support from a fuller explanation or data table. AI can draft that text only when the underlying values and intended finding are available. Do not let it invent a trend from an unclear screenshot.

Check errors against real interface behavior#

“Try again later” is useful only when retrying later can resolve the issue. If a required field is missing, identify the field and what is needed. If an upload failed, state whether the user's work was preserved when the system can confirm that state.

The content review should flag questions for engineering: is the error associated with the field, is it announced appropriately, and can a keyboard user reach and correct it? A well-written sentence alone does not answer those implementation questions.

Complete the checks AI cannot perform from text#

Review keyboard operation, focus order, zoom, contrast, accessible names and dynamic announcements using methods appropriate to the interface. Include people with relevant access needs in research where possible. Keep a record of observed problems and actual fixes rather than a generated claim that the page passes.

For error wording, use AI error message microcopy. For evaluating task behavior, continue with usability test analysis. W3C's evaluation overview explains the broader evaluation work that a content review supports.

Common questions#

Can AI certify that a page meets WCAG?#

An AI content review cannot certify conformance. It can identify potential issues, but evaluation needs appropriate technical inspection, manual checks and evidence about actual behavior.

Should every image have a detailed description?#

The alternative should match the image's purpose. Informative and functional images need useful equivalents; decorative images can require an empty alternative rather than extra narration.

Is plain language enough to make a form accessible?#

Clear wording helps, but labels, semantics, keyboard behavior and error handling also matter. Review the text and the implemented interaction together.

Download the working files#

Download the copyable prompt and download the illustrative worksheet (CSV). Replace example data with your own verified inputs; the worksheet is a planning aid.

Browse the English AI workflow guides for related tasks.