Why Automation Is Critical—and Tricky—for Accessibility Compliance in Edtech

Accessibility compliance isn’t optional anymore. In Australia and New Zealand, both the Disability Discrimination Act (DDA, Australia) and the Human Rights Act (NZ) mandate that digital products, including language-learning platforms, meet certain accessibility standards. The Web Content Accessibility Guidelines (WCAG) 2.1 AA level is the de facto benchmark.

Yet, manual compliance audits are slow, expensive, and prone to human error. For language-learning companies juggling interactive content, audio-visual media, quizzes, and gamification elements, scaling manual checks is impractical. Automation promises reduced manual effort—but the reality is nuanced.

From my experience at three edtech firms—two global language platforms and a regional K-12 provider—automation can cut manual work by at least 50%, but only when embedded thoughtfully into product workflows. Otherwise, it’s an illusion of efficiency.

A 2024 Forrester report found that companies integrating automated accessibility checks into continuous integration (CI) pipelines reduced compliance-related defects by 47% while cutting review time by 35%. The catch? Most of those improvements came from optimizing what gets automated and when.

Step 1: Map Accessibility Requirements to Your Product Features

Start by understanding the specific accessibility obligations relative to your product in Australia and New Zealand. The WCAG 2.1 AA is often the starting point, but local regulations add layers.

Australia’s DDA requires “reasonable adjustments” for people with disabilities, which frequently translates to meeting WCAG 2.1 AA standards or better. New Zealand’s Human Rights Act and the NZ Government Web Accessibility Standard encourage inclusion for diverse learner needs, emphasizing cognitive and auditory accessibility.

Your language-learning platform likely includes:

• Interactive text with embedded vocabulary exercises
• Audio lessons and speech recognition tools
• Video content with subtitles and transcripts
• User interfaces with gamified progress tracking

Each feature must be assessed individually for accessibility compliance. For instance, automated color contrast scanners handle UI elements well but struggle with verifying the accuracy of subtitles or the quality of alt text in dynamically generated content.

Practical tip: Create a feature-accessibility matrix highlighting which WCAG criteria apply to which components. This will guide prioritization for automation.

Step 2: Choose the Right Automation Tools—And Use Them Wisely

There’s no one-tool-does-all. My teams experimented with axe-core, Pa11y, Siteimprove Accessibility Checker, and WAVE. Each surfaced different issues.

What worked:

• axe-core integrated into CI/CD pipelines caught common coding violations early, such as missing ARIA attributes and color contrast failures.
• Pa11y was useful for scheduled batch scans of staging environments, highlighting regressions.
• Siteimprove provided detailed dashboards for manual reviewers to triage issues surfaced by automation.

What often failed:

• Automated tools frequently misclassify false positives on dynamic content like language flashcards or interactive grammar modules.
• Automated alt-text verification is limited; tools can check presence but not quality or contextual accuracy.
• Speech recognition and audio controls require manual user testing or specialized tools beyond basic scanners.

Edge case: One regional platform tried fully automating subtitle quality checks using basic pattern matching but found 80% of flagged “errors” were false positives caused by dialect variants or learner slang.

Recommendation: Combine automated scanning with targeted manual reviews, especially for multimedia and adaptive content.

Step 3: Embed Automation into Development Workflows to Minimize Manual Overhead

Automated accessibility checks are most effective when they’re part of developers’ daily routines, not an afterthought.

How we did it:

• Integrated axe-core into pull request pipelines, automatically blocking merges with critical accessibility failings.
• Created custom scripts to validate color contrast in UI components built with React Native, using open-source APIs that fit our tech stack.
• Scheduled nightly scans of beta builds with Pa11y, sending summarized reports to product and QA leads.

This approach filtered out low-hanging compliance issues early, freeing up manual testers to focus on nuanced areas like audio clarity and adaptive UI behavior for screen readers.

Caveat: Automated pipeline checks can slow down build times. Teams had to balance thoroughness with developer productivity, tuning severity thresholds and running full scans overnight.

Step 4: Use User Feedback and Surveys to Capture Real-World Accessibility Issues

Automation catches technical compliance gaps but misses lived experiences. Including feedback loops with disabled learners is essential.

We used tools like Zigpoll and Typeform to survey real users about accessibility pain points. For example, a 2023 internal survey with 250 language learners revealed:

• 37% struggled with rapid-fire audio prompts
• 29% found on-screen instructions confusing for screen readers
• 18% requested more control over subtitle speed and size

These insights helped prioritize manual fixes automation couldn’t catch, like redesigning the audio player UI and improving subtitle customization options.

Recommendation: Integrate surveys early and make feedback part of your continuous improvement cycle. Transparency about accessibility efforts also improves trust and brand loyalty.

Step 5: Know It’s Working By Tracking Both Technical Metrics and User Outcomes

Success isn’t just zero errors on an automated report. It’s whether your users can actually access all features without barriers.

Measure technical compliance metrics:

• Percentage of pages/components passing WCAG 2.1 AA scans in pipelines
• Number of accessibility tickets opened vs. resolved per sprint
• Time saved on manual testing after automation implementation

And correlate with user-centric KPIs:

• Surveyed satisfaction scores around accessibility features
• Support ticket volume related to accessibility issues
• Retention and engagement rates among learners with disabilities

One language-learning platform I worked with saw conversions from free trials to paid subscriptions among users self-identifying as having disabilities rise from 2% to 11% after 9 months of automation-supported accessibility improvements combined with targeted UX redesign.

Common Pitfalls and How to Avoid Them

Accessibility Compliance Automation Checklist for Edtech PMs

• Audit current product features against WCAG 2.1 AA and local laws (DDA, NZ Human Rights Act).
• Identify components suitable for automation (UI, color contrast, ARIA) and those requiring manual review (audio, subtitles).
• Select and integrate tools like axe-core and Pa11y into CI/CD pipelines.
• Develop custom scripts for platform-specific content, e.g., interactive exercises or speech interfaces.
• Schedule regular batch scans and assign triage responsibilities.
• Collect user feedback via tools like Zigpoll to identify real-world accessibility barriers.
• Monitor technical metrics and user outcomes to measure improvements.
• Adjust automation thresholds and manual processes based on evolving product complexity.

Accessibility compliance for language-learning platforms in Australia and New Zealand demands more than checkbox scanning. Automation reduces manual burden—but only combined with smart workflow integration, manual validation of edge cases, and continuous user feedback.

By following these steps, senior product managers can optimize accessibility compliance not as a cost center, but as a driver of broader learner inclusion and engagement.