Can AI-Generated Accessibility Fixes Be Trusted?

When we talk to faculty about AI-powered accessibility tools, the same question comes up repeatedly: "How do I know the fixes are actually correct?"

It's a fair question. You're being asked to trust an algorithm with legal compliance and student access. This guide explains how AI accessibility fixes work, where they're reliable, and when you should double-check.

The Short Answer

Yes, but verify the things that matter.

AI accessibility fixes aren't magic—they're pattern matching at scale. Modern systems are excellent at some tasks (95%+ accuracy) and merely good at others (70-85%). Knowing the difference lets you use AI efficiently without blind trust.

Where AI Excels (95%+ Accuracy)

1. Reading Order Detection

AI is remarkably good at determining how text should be read on a page. It identifies:

• Multi-column layouts
• Sidebars vs. main content
• Headers, footers, and footnotes
• Figure and table positions

Why it works: These are visual patterns with consistent rules. AI has seen millions of document layouts.

Trust level: Very high. AI often catches reading order issues that humans miss.

2. Heading Structure

AI reliably identifies what text should be Heading 1, Heading 2, etc., based on:

• Font size and weight
• Visual hierarchy
• Semantic context

Why it works: Heading structure follows predictable visual cues.

Trust level: High. Minor adjustments may be needed for edge cases.

3. Table Header Detection

AI accurately identifies which cells are headers vs. data cells by analyzing:

• Position (top row, left column)
• Formatting (bold, background color)
• Content type (text labels vs. data values)

Why it works: Tables follow standard conventions that AI recognizes.

Trust level: High for standard tables. Complex merged-cell tables may need review.

4. Mathematical Equation Conversion

AI converts LaTeX and equation images to MathML with high accuracy for:

• Standard mathematical notation
• Common scientific formulas
• Statistical expressions

Why it works: Mathematical notation is formal and unambiguous.

Trust level: High. Always verify complex or unusual notation.

Where AI Is Good (70-85% Accuracy)

1. Alt Text for Simple Images

AI generates accurate alt text for:

• Charts and graphs (describes data trends)
• Diagrams (identifies components and relationships)
• Photos (describes scene, people, objects)

Where it can miss:

• Subject matter expertise (a biology diagram may need domain-specific terms)
• Educational context (what aspect of the image is relevant to your lesson?)
• Symbolic meaning (a photo chosen for emotional impact, not literal content)

Trust level: Good baseline. Review images that are central to your teaching.

2. Color Contrast Fixes

AI identifies and fixes low contrast text, but may:

• Change colors in ways you didn't expect
• Not match your institutional branding perfectly
• Occasionally make overly aggressive changes

Trust level: Good. Review visual appearance after fixes.

3. PDF Tagging

AI can add proper tags to untagged PDFs, but:

• Complex layouts may confuse it
• Scanned documents require OCR (which adds error potential)
• Unusual document structures may get mis-tagged

Trust level: Good for standard documents. Complex PDFs may need spot-checks.

Where Human Review Matters Most

1. Alt Text for Complex Educational Images

If an image is central to understanding your content, review the AI-generated alt text:

Example: A biology diagram of cellular respiration

• AI says: "Diagram showing cellular process with arrows and labels"
• Better: "Diagram of cellular respiration showing glucose entering mitochondria, ATP production, and CO2 release with labeled intermediate steps"

The AI described what it sees. You need to describe what students should learn.

2. Contextual Descriptions

AI doesn't know your teaching goals. A photo in a history lecture might need:

• AI's description: "Black and white photograph of city street with people walking"
• Your description: "Depression-era bread line in New York City, 1932, showing approximately 50 men waiting for food assistance"

3. Decorative vs. Informative Images

AI can't always tell if an image is:

• Decorative (should be marked as such, no alt text needed)
• Informative (needs descriptive alt text)

A colorful divider image? Decorative. A photo illustrating a concept? Informative. You know which is which; AI might not.

How to Use AI Fixes Efficiently

The 80/20 Approach

1. Let AI handle the bulk work (heading structure, reading order, tables, basic alt text)
2. Spot-check random samples (open 3-4 fixed documents, scan for obvious errors)
3. Focus human attention on what matters (key educational images, complex diagrams)

Red Flags to Watch For

• Generic alt text: "Image of chart" or "Figure 1" — AI couldn't interpret it
• Overly literal descriptions: Missing the educational point of an image
• Unusual document structures: Complex layouts, unusual formats

Green Flags (AI Probably Got It Right)

• Specific, detailed descriptions: AI saw and described actual content
• Proper heading hierarchy: H1 → H2 → H3 structure is logical
• Tables have headers marked: Row and column headers identified

Why AI Beats Manual Work (Even at 80% Accuracy)

Let's be realistic about the alternative:

Manual accessibility remediation:

• Takes 45-90 minutes per document
• Requires learning WCAG guidelines
• Is mind-numbingly tedious
• Often doesn't happen at all (faculty are busy)

AI-assisted remediation:

• Takes 2-5 minutes per document
• Handles 80-95% of issues automatically
• You review only what matters
• Actually gets done

A document with 80% of issues fixed automatically and 20% fixed by quick human review is infinitely more accessible than a document that never got fixed because no one had time.

How Aelira Builds Trust

We designed Aelira with verification in mind:

1. Before/After Preview

See exactly what changes AI made before you accept them. Toggle between original and fixed versions.

2. Confidence Scores

AI reports confidence levels for each fix. Low confidence? It flags it for your review.

3. Issue Categories

Know whether an issue was auto-fixed (high confidence) or flagged for review (needs your input).

4. Validation Re-Scan

After fixes are applied, run another scan to verify issues were actually resolved.

The Bottom Line

AI accessibility fixes are trustworthy for most common issues. They're not perfect, but they're:

• Faster than manual work (95%+ time savings)
• More consistent than tired humans
• Better at catching technical issues (reading order, heading structure)
• Good enough to achieve compliance

Your role: Review what matters educationally. Trust AI for the technical heavy lifting.

---

Want to see AI accessibility fixes in action? Try the demo — upload a document and see exactly what it detects and how it proposes to fix it. No signup required.