What problem were you solving?
Accessibility testing often generates large amounts of information. I explored whether AI could organize that information and help reviewers identify areas that deserved closer attention.
Prototype
Accessibility AI Proof of Concept
This proof of concept explored how large language models could support accessibility workflows by helping engineers review results, identify patterns, and surface potential issues earlier in the development process.
AccessibilityAIAutomationPython
Overview
The objective was not to automate accessibility entirely, but to reduce repetitive review work and help teams focus on higher-value analysis.
Goals
- Review accessibility validation output
- Organize large volumes of test results
- Summarize findings
- Surface potential accessibility issues
- Reduce repetitive manual review
Technical Focus
- AI-assisted accessibility workflows
- Prompt engineering
- Python automation
- Screen reader transcript analysis
- Accessibility testing
- Workflow optimization
Challenges
Accessibility depends heavily on context, making it difficult for AI to determine whether an experience is genuinely usable. One of the biggest challenges was distinguishing between objective issues and situations that still required human evaluation.
What I Learned
AI performs best as an assistant rather than a replacement. The most valuable workflows combined automation with human expertise, allowing engineers to spend less time reviewing repetitive output and more time solving accessibility problems.
Interview Q&A
Would you trust AI to replace accessibility testing?
No. Accessibility requires human judgment and real user understanding. AI is most effective as a productivity tool that helps engineers review information more efficiently.
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Let's Build Something Great Together
I'm always happy to chat about frontend engineering, accessibility, React, or interesting opportunities.