Note: The creation of this article on testing Help was human-based, with the assistance on artificial intelligence.
Explanation of the success criteria
WCAG 3.3.5 Help is a Level AAA conformance level Success Criterion. It ensures that users encountering errors, confusion, or complex tasks can access clear, actionable guidance tailored to the situation.
Effective help can take the form of inline instructions, tooltips, support links, or guidance dialogs that anticipate user needs and reduce frustration. By prioritizing Help, organizations not only improve accessibility for users with cognitive, learning, or sensory challenges but also enhance overall usability and task completion for all users. Testing and implementing Help consistently reflects a commitment to proactive, user-centered design, turning potential barriers into opportunities for empowerment and efficiency.
Who does this benefit?
- Users with cognitive or learning disabilities: Gain clear, contextual guidance that helps them understand and complete tasks without frustration.
- Users with sensory impairments: Benefit from accessible instructions delivered via multiple modalities, such as screen readers or visual cues.
- All users navigating complex processes: Experience reduced errors and smoother task completion thanks to proactive, context-aware assistance.
Testing via Automated testing
Automated testing excels at quickly identifying structural and technical issues, such as the presence of ARIA attributes, focusable elements, or linked help content. It provides broad coverage and repeatable results, making it ideal for regression testing, yet it cannot judge the clarity, relevance, or context-sensitivity of help content.
Testing via Artificial Intelligence (AI)
AI-based testing introduces nuanced analysis, leveraging natural language processing to evaluate the readability, relevance, and tone of help text, and can flag inconsistent or potentially confusing instructions. However, AI may misinterpret context-specific guidance, occasionally producing false positives or missing culturally or cognitively sensitive issues.
Testing via Manual testing
Manual testing remains indispensable for evaluating the real-world effectiveness of help mechanisms. Human testers can simulate diverse user journeys, assess whether guidance is actionable, and determine if instructions actually reduce errors and confusion. The drawback is that manual testing is time-intensive, subjective, and harder to scale.
Which approach is best?
A robust, hybrid approach to testing WCAG 3.3.5 Help leverages the complementary strengths of automated, AI-based, and manual testing to ensure help mechanisms are both present and effective.
Start with automated testing to rapidly verify the technical foundations: confirm that help links, ARIA attributes, focusable elements, and error-related guidance are correctly implemented and accessible.
Next, layer AI-based testing to evaluate the quality, clarity, and context relevance of help content, using natural language processing to flag ambiguous instructions, inconsistent messaging, or overly complex language that could confuse users.
Finally, conduct manual testing with human participants representing diverse abilities and experiences, simulating realistic user scenarios to determine whether help is truly actionable, timely, and effective in reducing errors. This phase also captures nuanced insights on tone, usability, and cultural appropriateness that neither automation nor AI can fully assess.
By combining these three approaches, organizations achieve a rigorous, scalable, and deeply human-centered assessment, ensuring that help features are not only technically compliant but genuinely supportive for every user.
Related Resources
- Understanding Success Criterion 3.3.5 Help
- mind the WCAG automation gap
- Providing a help link on every web page
- Providing help by an assistant in the web page
- Providing spell checking and suggestions for text input
- Providing text instructions at the beginning of a form or set of fields that describes the necessary input
- Providing expected data format and example