Gains:
- Ability to control and improve color contrast, alternative text, keyboard access and WCAG criteria with artificial intelligence support
- Ability to generate and verify accessibility texts such as screen reader experience, alt text and form labels with artificial intelligence
- Understanding the limit of validating AI's accessibility recommendations with real assistive technology and user testing
Accessibility (a11y for short) is the ability of a product to be used by everyone, including individuals with disabilities: a visually impaired user can navigate with a screen reader (auxiliary software that converts text to speech), a person with motor difficulties can do anything with a keyboard, a colorblind person can receive information without relying on color. Inclusive design is broader: it puts human diversity at the center of design, including age, language, culture, temporary disability (broken arm) or contextual disability (screen in the sun). Accessibility is not an “extra” but a fundamental responsibility and a legal requirement in most countries. AI is a powerful prescreener and draft generator in this field; but true accessibility is only confirmed by actual assistive technology and user testing.
WCAG and key control areas
WCAG (Web Content Accessibility Guidelines) is a set of internationally accepted criteria for accessibility; AA level is generally targeted. It has four principles: the content must be perceivable, the interface must be usable, the information must be understandable and technically sound. The most common control areas in practice are:
- Color contrast: Is the difference between text and background sufficient? (For AA, at least 4.5:1 ratio in normal text.)
- Alternative text (alt text): Do the images have a text equivalent that explains them to the screen reader?
- Keyboard access: Can anything be done without a mouse? Does the focus order make sense?
- Color-only information: Phrases like "Fill in red fields" exclude the colorblind user.
- Form labels: Does each input field have a label that the screen reader will read?
- Touch target: Are the buttons large enough to be pressed comfortably with fingers?
AI can do a quick preliminary scan in many of these areas: you can give some text and ask "is the contrast sufficient?", give an image description and ask "suggest alt text", give an interface description and ask "what are the keyboard access issues".
Caution: Just because the AI says "it looks accessible" is not a guarantee of accessibility. The automatic check catches only some of the WCAG errors; The rest will emerge with actual use.
Alt text: the secret of good alt text
Alt text replaces the image for the visually impaired user. Good alt text conveys the function and meaning of the image, not its decorative detail. The alt text for an "add to cart" icon should be "Add to cart" and not "shopping cart image" because it's the action that matters to the user. AI is good at generating subtext outlines, but because it doesn't know the context, it may produce overly descriptive or irrelevant text. You ask each alt text “why is this image here?” Crop with the question.
visual
Weak subtext
Strong subtext
Cart icon (button)
"Cart icon, gray color"
"Add to cart"
product photo
"picture"
"Blue winter coat, front view"
decorative line
"Ornament line"
(leave blank — decorative)
Graphics
"Graphic image"
"2024 sales: increase every quarter"
Inclusive language and scope
Accessibility is not limited to technical controls; Language is also inclusive. Text that assumes gender (“user and his/her spouse”), excludes based on ability (“glance,” “hear easily”), or contains cultural assumptions will exclude some users. The AI can scan the text from this perspective, but you must ensure that the “neutral” language it suggests remains natural and understandable; Overcorrecting can make text awkward.
three mini cases
Case 1 — Contrast errors caught early. A team had AI scan the text colors of 20 screens and determined that the contrast was below the AA threshold in 7 places. The corrections were made without going into development; the cost of subsequent correction was avoided. But the team still didn't skip the actual screen reader test.
Case 2 — Only color-related information was corrected. One form showed error fields only with a red border. The AI flagged this; The team also added text and icons to each error. Colorblind users could now see the error. Lesson: color alone cannot convey information.
Case 3 — AI misled approval. One designer skipped screen reader testing because he called the AI “accessible.” In the real test, it turned out that the focus order was confused and some buttons were not read at all. Lesson: automatic confirmation is the beginning; actual assistive technology testing is mandatory.
Copiable prompts
Pre-scan this interface description for accessibility:1) is there information carried solely based on color?2) is there a text label for each clickable element?3) are there any elements that cannot be accessed via keyboard?4) does the focus order make sense?List each problem and suggestion. Add note "Real test required".Recipe: <<text>>
Suggest alt text for these images. Rule: convey the FUNCTION/meaning of the image, not the decorative detail. Write the action for button icons. For decorative images, say "alt text should be left blank". Context and image descriptions: <<list>>
Check these texts for inclusive language: are there gender assumptions, ability-based exclusionary language (such as “see,” “hear”), cultural assumptions? Suggest alternatives that remain natural; do not overcorrect.Texts: <<list>>
Write accessible error and label text for this form: visible label for each field, description for the screen reader, and message describing the error, regardless of color (text + icon). Voice and tone: <<card>>Form fields: <<list>>
Weak prompt / Strong prompt
Weak: "Write alt text for this image."
The result: “An image” or overly descriptive text that misses the function.
Strong: "Suggest alt text for these images; convey the function/meaning of the image; write the action for button icons; mark decorative ones as 'must be left blank'."
The result: Contextual, function-oriented, accurate subtexts.
Difference: strong prompt brings functional focus + button convention + decorative distinction.
Common mistakes
- Mistaking artificial intelligence approval as a guarantee of accessibility. It is not a substitute for real testing.
- Just loading information into color. Colorblind users miss information.
- Explaining the image, not the function, in the alt text. An action must be written for the button icon.
- Leaving accessibility for last. If it is not started at the wireframe stage, it will be expensive to patch it later.
- Overcorrected language. Inclusive language that loses naturalness also impairs comprehensibility.
In summary
Accessibility means that the product is available to everyone; It is not an extra, it is an essential and, in most places, a legal responsibility. AI is valuable as a quick preflight and draft generator for contrast, alt text, keyboard access, and inclusive language scanning. But automatic approval catches only some of the WCAG errors; Actual accessibility is confirmed by testing with screen readers and real assistive technology users. Use the model as a front browser, get the proof from real testing.
Application task
- Pre-scan an interface description for accessibility at the first prompt.
- Correct only color-based information or unlabeled items.
- Generate function-oriented alt texts for the visuals on your screen with the second prompt.
- Check your texts for inclusive language with the third prompt.
- If possible, try the actual test with a screen reader and note what the automatic scan misses.
checklist
- [ ] I pre-scanned contrast, keyboard access and labels.
- [ ] I did not leave information based solely on color.
- [ ] I wrote the subtexts in a functional way and left the decorative ones blank.
- [ ] I made an inclusive language control, I preserved the naturalness.
- [ ] I did not consider automatic approval a guarantee of accessibility.
- [ ] I planned/implemented actual assistive technology testing.