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A polished AI-generated mockup is not proof of an accessible or usable interface. A static image can reveal visible issues such as weak contrast, unclear hierarchy, or small controls, but it cannot establish keyboard access, semantic structure, focus behavior, or whether someone can complete a task. Evaluate the mockup visually, test a working prototype for behavior, and use usability testing—including people with disabilities where possible—to assess real task completion.

What a mockup can—and cannot—tell you

Use WCAG 2.2 as the current reference for web accessibility. The W3C advises using the most current version, and its success criteria are written as testable, technology-independent statements. But many criteria depend on behavior or semantic implementation that a screenshot does not expose. See the WCAG 2.2 Recommendation.

Review method What it can establish What it cannot establish by itself
Static mockup review Visible hierarchy, contrast, text spacing, apparent target size, and whether the content specification indicates text alternatives for meaningful images. Keyboard operation, focus order and visibility during interaction, programmatic names and semantics, error behavior, responsive implementation, or task completion.
Working prototype testing Interaction behavior, keyboard navigation, focus, labels and semantics, error handling, and responsive states in the tested implementation. Whether a representative population can use the product successfully across all relevant contexts without user testing and adequate coverage.
Usability testing Whether participants can complete representative tasks, where they encounter friction, and how the experience works for the people included in the sessions. Universal usability or WCAG conformance across untested users, pages, states, and technologies.

Keep these evidence types distinct. A visual inspection is useful, but it is not a WCAG conformance evaluation; prototype checks and user testing answer different questions.

Define the scope before reviewing screens

Decide what product experience the review covers before selecting screens. WCAG-EM 2.0 provides a repeatable evaluation process and calls for a defined scope and conformance target. Its sampling guidance also recognizes that interaction, generated content, adaptation, and inconsistency can require broader coverage. See the WCAG-EM 2.0 methodology.

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  • Product boundary: Identify the pages, flows, embedded components, and technologies included. State what is excluded.
  • Conformance target: If you intend to make a conformance claim, name the WCAG version and target level. Do not imply conformance based on a visual review alone.
  • Representative content and states: Include relevant content variants, empty and error states, and states reached after interaction—not only the strongest-looking generated screen.
  • Sampling depth: Review more outputs when screens vary between prompts or sessions, content is generated, layouts adapt, interaction is complex, or consistency is low. A simple, consistent experience may need a smaller sample than a variable one.

Inspect the visible design systematically

Review each selected screen against criteria that can be assessed from its visual presentation. Record the exact screen and element, the evidence, the likely impact, and a severity rating defined by your team. Do not treat a single score as proof of conformance.

Information hierarchy and task clarity

Check whether headings, instructions, status information, and primary actions are visually distinguishable and ordered in a way that supports the intended task. Ask whether a user can tell what the screen is for and what action comes next. Visual hierarchy can suggest clarity, but only task testing can show whether people understand and complete the flow.

Contrast and use of color

Inspect text and controls for sufficient contrast, and check that color is not the sole way to communicate meaning, status, or errors. A screenshot may expose likely problems, but a definitive contrast check needs the actual colors and relevant WCAG criterion; visual estimation alone is not enough.

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Text spacing and readability

Check whether text remains legible and whether the layout appears resilient to increased text spacing. A static composition can reveal cramped typography or overflow risks, but testing the implementation with adjusted text spacing is needed to see whether content or functionality is lost.

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Target size and content alternatives

Look for controls that appear difficult to select and note whether the design specifies meaningful alternatives for images that convey information. A screenshot can show apparent dimensions and content intent; it cannot establish implemented accessible names, semantics, or how a target behaves in use.

Test the working prototype and representative tasks

Once there is an interactive implementation, move beyond appearance. Test behavior using the technologies and states that matter to the product, then invite representative users—including people with disabilities and assistive-technology users where possible—to attempt realistic tasks.

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  • Keyboard operation: Can users reach and operate interactive elements without a pointer? Is the focus order sensible and focus visible?
  • Names and semantics: Do controls expose meaningful labels, roles, and states to assistive technology, rather than relying only on visual text or position?
  • Errors and feedback: Are errors identified and explained, and can users understand how to correct them? Do status changes and confirmations reach users who need them?
  • Responsive states: Does the experience remain usable at relevant viewport sizes and when content reflows or changes?
  • Task completion: Can participants understand the goal, find the next action, recover from mistakes, and finish the task? Record where they hesitate, fail, or need assistance.

For each test, note the environment, relevant assistive technology, task, observed result, and any limitations. A prototype test covers the implementation and conditions actually tested; user sessions add evidence about the participants and tasks included, not a guarantee for everyone.

Use AI prompts and AI critique as aids, not evidence of conformance

It is reasonable to include explicit accessibility requirements in a generation prompt and compare the resulting mockups with outputs produced under a baseline prompt. A 2025 Web Conference study compared five AI design tools using baseline and accessibility-oriented prompts, assessing criteria visible in static images, including color use, contrast, text spacing, and target size. That study does not establish a universal pass rate, a best tool, or a general causal effect for accessibility prompting. Review every output against the criteria you selected. See the 2025 Web Conference study.

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AI-generated critique can also help reviewers surface questions, but it is not certification. A 2024 preprint assessed 51 UI mockups, compared model suggestions with human expert suggestions, and consulted 12 expert designers about fit with practice. This supports using AI feedback as an additional input—not a replacement for standards-based assessment or human validation. See the 2024 preprint on LLM-generated UI mockup feedback.

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More broadly, NIST’s ARIA Evaluation Planning Manual describes holistic AI evaluation as combining Model Testing, Red Teaming, and User Testing. It is an AI evaluation resource, not a mockup-specific accessibility checklist. NIST’s voluntary AI Risk Management Framework addresses trustworthiness across AI design, development, use, and evaluation; NIST says its Generative AI Profile was released on July 26, 2024, and that AI RMF 1.0 is being revised. These resources can inform broader AI governance, but they do not show that a particular interface is accessible. See the NIST ARIA Evaluation Planning Manual and NIST AI Risk Management Framework.

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Record findings so they can be fixed and verified

Log issues individually rather than relying on an unexplained overall grade. For each finding, capture:

  • Location: Screen, state, and element.
  • Criterion: The relevant WCAG criterion or other stated review criterion, where applicable.
  • Evidence: What was observed, under what conditions, and how to reproduce it.
  • Impact: The barrier or task consequence for users.
  • Severity: A defined rating that distinguishes minor friction from a complete barrier.
  • Disposition: Whether the issue needs a design correction, implementation fix, more testing, or a documented scope decision.

A 2025 study of static AI-generated interfaces assessed visual hierarchy, color contrast, text spacing, and target size against selected WCAG 2.1 criteria and used a five-point scale from 0 through 4, ranging from no violation to complete barriers. That was the authors’ study method, not an industry-wide benchmark. If your team adopts a scale, define its levels and use them consistently; do not compare scores from different methods as if they were equivalent. See the 2025 DIS study of static AI-generated UIs.

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Compare mockups or tools on more than appearance

When choosing between generated alternatives, compare the same tasks, states, and review conditions. Output speed alone does not establish accessible quality.

Comparison area What to examine
Task clarity Can a representative user identify the next action and complete the intended task?
Visible accessibility Contrast, meaningful non-color cues, text spacing, apparent target size, and clear hierarchy.
Interaction behavior Keyboard navigation, visible focus, control names and semantics, error handling, and responsive behavior in the implementation.
Coverage and consistency How many screens and states were sampled, and whether variants repeat or regress on criteria.
Evidence quality Traceable examples, defined severity, reviewer agreement, and clear limits on what was not tested.
Iteration cost How many manual corrections are needed after generation, and whether fixes introduce regressions elsewhere.

What to include in an evaluation report

A useful report lets someone understand what was examined and what the findings do—and do not—support. Include the evaluation date, WCAG version and target level if relevant, product scope, technologies relied on, samples and states reviewed, findings with evidence and severity, and known limitations. If the work was limited to static screens, say so plainly and identify the behavioral questions that remain untested.

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