Note: The creation of this article on testing Error Prevention (All) was human-based, with the assistance on artificial intelligence.
Explanation of the success criteria
WCAG 3.3.6 Error Prevention (All) is a Level AAA conformance level Success Criterion. It focuses on safeguarding users from making serious mistakes before submitting information, particularly when the consequences are significant, such as legal commitments, financial transactions, or the permanent loss of data. This success criterion requires that forms and processes provide users with mechanisms to review, confirm, and correct their input prior to final submission.
In practice, this means implementing features like confirmation pages, clear summaries of entered data, undo or revision options, and explicit warnings before irreversible actions occur. From an accessibility standpoint, this criterion acknowledges that users with cognitive or motor disabilities are especially vulnerable to inadvertent errors. By building in proactive safeguards, designers not only reduce the risk of costly mistakes but also promote trust, confidence, and inclusivity across all user interactions.
Who does this benefit?
- Users with cognitive disabilities: Benefit from clear confirmation steps and review options that prevent confusion and accidental submission of incorrect data.
- Users with motor impairments: Gain protection against unintended clicks or taps that could trigger irreversible actions or data loss.
- Users with low vision or on-screen magnifiers: Rely on confirmation screens and review summaries to ensure they haven’t missed important details while navigating or entering information.
- Older adults: Appreciate the additional verification steps that help them feel confident when performing complex online tasks such as payments or applications.
- All users performing critical tasks: Experience greater trust and peace of mind when completing high-stakes transactions involving legal, financial, or permanent data.
Testing via Automated testing
Automated testing offers efficiency and consistency, it can flag missing confirmation dialogs, identify irreversible actions without review steps, or detect the absence of warning messages before critical submissions. However, its reach is inherently limited; automation cannot assess whether a confirmation step is clear, accessible, or contextually appropriate. It often fails to recognize the intentionality behind user safeguards, leaving significant gaps in real-world assurance.
Testing via Artificial Intelligence (AI)
AI-based testing brings a more dynamic layer of analysis by using machine learning to identify patterns in form design, detect likely risk areas, and even simulate user flows that may lead to data loss or legal missteps. It excels at finding complex logic issues and predicting where users might encounter errors. Yet, AI still lacks human judgment, it cannot determine whether a warning is understandable to a person with cognitive disabilities or whether a confirmation flow builds genuine user confidence. AI insights are powerful, but they must be grounded in human review to avoid false positives or overgeneralizations.
Testing via Manual testing
Manual testing remains the most reliable method for evaluating WCAG 3.3.6 compliance. Human testers can experience the process as real users do, identifying friction points, unclear instructions, and emotional responses to irreversible actions. They can assess tone, clarity, timing, and accessibility across assistive technologies, ensuring that “error prevention” is not merely a technical checkbox but a truly inclusive safeguard. The trade-off, of course, is time and cost, manual testing requires skilled evaluators and thorough scenario design. However, the insight it provides is unmatched.
Which approach is best?
A hybrid approach to testing WCAG 3.3.6 Error Prevention (All) recognizes that safeguarding users from critical mistakes demands both technical precision and human empathy.
The process begins with automated testing, which efficiently scans for foundational elements, confirm dialog triggers, form validation structures, and warning messages before irreversible actions. These checks establish a baseline, ensuring no critical interaction is missing the essential scaffolding for error prevention.
From there, AI-based testing elevates the process by simulating user behaviors across multiple contexts, predicting where users might make irreversible errors, and identifying subtle risk patterns that automation alone overlooks. AI can model varied cognitive and motor responses, flagging potential friction points such as confusing confirmation flows or unclear review summaries.
The final, and most crucial, layer is manual testing, which brings the human lens to interpret context, clarity, and emotional reassurance. Skilled testers validate whether the confirmation steps are logical, accessible, and user-friendly, especially for people with cognitive, visual, or motor disabilities. They assess how well the design communicates risk, how easy it is to undo or correct actions, and whether the experience builds confidence rather than anxiety.
Together, these three methods form a comprehensive framework: automation ensures structural compliance, AI provides predictive intelligence, and manual testing confirms authentic usability. This integrated strategy transforms compliance testing into proactive risk management, protecting both the user’s trust and the organization’s integrity through accessible, human-centered design.
Related Resources
- Understanding Success Criterion 3.3.6 Error Prevention (All)
- mind the WCAG automation gap
- Providing the ability for the user to review and correct answers before submitting
- Providing the ability to recover deleted information
- Providing a checkbox in addition to a submit button
- Providing a stated time within which an online request (or transaction) may be amended or canceled by the user after making the request
- Requesting confirmation to continue with selected action