Note: The creation of this article on testing No Keyboard Trap was human-based, with the assistance on artificial intelligence.
Explanation of the success criteria
WCAG 2.1.2 No Keyboard Trap is a Level A conformance level Success Criterion. It ensure that users navigating with a keyboard are never trapped within a subsection of a page. In practice, this means focus must always be able to move freely into, within, and out of interactive elements. While this requirement is often viewed through a compliance lens, its true value lies in creating an inclusive, frustration-free experience for anyone who relies on a keyboard, whether due to blindness, physical disability, or simply personal preference.
Who does this benefit?
- People who use a keyboard or interface to navigate the web, including those who are blind or have physical disabilities.
Testing via Automated testing
Automation provides a strong first line of defense. It scales quickly across large codebases, integrates seamlessly into CI/CD pipelines, and identifies obvious risks like poor focus management or problematic tabindex practices. However, its power lies in breadth, not depth.
Automated tools cannot replicate true user interaction, which means they often generate false positives or overlook genuine traps. On their own, they provide useful signals but not definitive answers.
Testing via Artificial Intelligence (AI)
Artificial Intelligence enhances the process by bringing context and adaptability. AI can simulate interaction flows, identify high-risk components such as custom modals or embedded widgets, and surface insights beyond rule-based checks. Its predictive capability helps prioritize where human testers should focus.
Yet AI is not infallible. It may misinterpret intent, miss complex edge cases, or require human oversight to validate findings. Its accuracy depends as much on training data as on algorithms.
Testing via Manual testing
Manual testing is where accuracy and user perspective converge. A skilled tester navigating entirely by keyboard can confirm whether focus behaves predictably and exits components without friction. This method captures nuance that machines cannot, evaluating not just compliance but usability.
The challenge, of course, is scale: manual testing is time-intensive, requires expertise, and is difficult to apply exhaustively across large or dynamic environments.
Which approach is best?
No single approach for testing No Keyboard Trap is perfect. However, using the strengths of each approach in combination can have a positive effect.
Automated testing sets the baseline, catching obvious risks and providing continuous monitoring. AI builds on this by adding intelligence and prioritization, narrowing the focus to areas most likely to contain traps. Manual testing then delivers the final, definitive validation, ensuring that “no keyboard trap” is more than a technical box checked, but a guarantee of usability.