AI Readability Score: Measuring Machine Comprehension

AI readability score is a framework for measuring how easily AI systems can parse, understand, and extract useful information from your content.

Scoring Dimensions

Dimension	Weight	What It Measures
Structure	25%	Heading hierarchy, semantic HTML
Clarity	25%	Definition patterns, answer-first
Metadata	20%	Structured data, meta tags
Extractability	20%	Tables, lists, code blocks
Accessibility	10%	No-JS access, alt text, text content

Structure Score (0-25)

Check	Points
Single H1	5
Logical H2/H3 hierarchy	5
Semantic HTML elements	5
Article wrapper	5
No heading skips	5

Clarity Score (0-25)

Check	Points
Answer in first 2 sentences	10
Definition pattern used	5
No filler/marketing language	5
Entity names (not pronouns)	5

Metadata Score (0-20)

Check	Points
JSON-LD structured data	8
Meta description (120-160 chars)	4
Canonical URL set	4
Author information	4

Extractability Score (0-20)

Check	Points
Tables for comparisons	5
Numbered lists for procedures	5
Code blocks with language	5
FAQ section present	5

Accessibility Score (0-10)

Check	Points
Content accessible without JS	4
Images have alt text	3
No content in images only	3

Score Interpretation

Score	Rating	Action
80-100	Excellent	Maintain quality
60-79	Good	Minor improvements
40-59	Fair	Significant restructuring needed
0-39	Poor	Comprehensive overhaul required

Automated Checking

Key automated checks:

1. Parse HTML for heading hierarchy
2. Check for JSON-LD presence and validity
3. Verify meta description length
4. Count tables, lists, and code blocks
5. Test no-JS rendering

Related Articles

• AI Crawl Signals — Discovery signals
• HTML Semantic Structure for AI — HTML structure
• Markdown Optimization for AI — Markdown quality