Screen Reader Simulation

Agora's AI-powered screen reader simulation provides insights into how assistive technologies navigate your website. This feature requires an AI license and helps you understand the screen reader user experience.

Overview

Screen reader simulation in Agora provides:

• Virtual screen reader navigation that mimics real assistive technology
• Reading order analysis to understand content flow
• Accessibility attribute analysis for ARIA labels and descriptions
• Interactive element identification and usability assessment
• Content comprehension insights from an AI perspective

How Screen Reader Simulation Works

AI-Powered Analysis

Agora's screen reader simulation uses artificial intelligence to:

1. Parse Page Structure: Analyzes the DOM and accessibility tree
2. Simulate Navigation: Mimics how screen readers traverse content
3. Generate Insights: Provides AI-powered recommendations for improvements

Virtual Navigation

The simulation replicates common screen reader behaviors:

• Sequential reading through page content
• Landmark navigation using ARIA landmarks and headings
• Form interaction patterns and label associations
• Table navigation including header relationships
• Link and button identification and context

Accessing Screen Reader Features

License Requirements

Screen reader simulation requires:

• ✅ Active Agora License: Basic Agora functionality
• ✅ Active AI License: Required for AI-powered features
• ✅ Internet Connection: AI processing requires cloud connectivity
• ✅ Available API Credits: AI analysis consumes API credits

Enabling Screen Reader Analysis

1. Navigate to Live scan: Load a page you want to analyze
2. Open Screen Reader Panel: Select 'Screen reader paths' from the dropdown
3. Start Analysis: Click "Analyze" button to begin AI processing
4. Review Results: Explore the generated insights and navigation path

Screen Reader Panel Interface

Navigation Visualization

Reading Order Display

The simulation shows the exact order screen readers follow:

• Numbered sequence indicating reading order
• Visual highlights showing current focus position
• Content grouping by landmarks, headings, links or form elements

Content Analysis Results

Reading Experience Assessment

• Content Flow: How naturally content reads in sequence
• Context Clarity: Whether content makes sense without visual cues
• Information Hierarchy: How well structure conveys importance
• Navigation Efficiency: How easily users can find specific content

Identified Issues

• Missing Context: Content that doesn't make sense when read aloud
• Poor Reading Order: Logical flow problems in content sequence
• Inadequate Labels: Interactive elements without clear purposes
• Structural Problems: Heading hierarchy or landmark issues

AI Insights and Recommendations

Accessibility Attribute Analysis

The AI evaluates accessibility attributes and their effectiveness:

ARIA Label Assessment

• Clarity: Whether ARIA labels are descriptive and useful
• Context: If labels provide sufficient context for interaction
• Redundancy: Identifying unnecessarily verbose or repetitive labels
• Missing Labels: Interactive elements that lack proper labeling

Description Quality

• ARIA Descriptions: Evaluation of aria-describedby content usefulness
• Alt Text Quality: Assessment of image alternative text descriptiveness
• Link Context: Whether link text clearly indicates destination or purpose
• Button Purpose: Clarity of button text and intended actions

Content Structure Insights

Heading Hierarchy Analysis

• Logical Flow: Whether heading levels follow proper sequence
• Content Organization: How well headings organize page content
• Navigation Value: Effectiveness of headings for screen reader navigation
• Missing Headings: Areas where headings would improve navigation

Landmark Usage

• Landmark Appropriateness: Whether landmarks are used correctly
• Content Organization: How landmarks structure page content
• Navigation Efficiency: How landmarks help users orient and navigate
• Missing Landmarks: Areas that would benefit from landmark identification

Practical Applications

Development Workflow Integration

Early Design Review

• Content Planning: Use insights to plan accessible content structure
• Information Architecture: Optimize content organization for screen readers
• Interaction Design: Design interactions that work well with assistive technology

Development Testing

• Code Validation: Verify that accessibility code works as intended
• Label Verification: Ensure ARIA labels and descriptions are effective
• Navigation Testing: Confirm logical reading order and navigation paths

Quality Assurance

• Pre-Launch Testing: Final verification of screen reader experience
• Regression Testing: Ensure changes don't break screen reader functionality
• User Acceptance: Validate that the experience meets accessibility standards

Limitations and Considerations

AI Simulation vs. Real Screen Readers

What the Simulation Captures

• Basic navigation patterns common across screen readers
• Content reading order and structure
• Accessibility attribute interpretation
• Common user interaction patterns

What It Cannot Fully Replicate

• User Preferences: Individual screen reader configuration variations
• Software Differences: Specific behaviors of different screen reader brands
• User Expertise: How experienced users customize their navigation
• Contextual Understanding: Nuanced human comprehension of content

Best Practices

Using Screen Reader Insights Effectively

Focus on High-Impact Issues

• Navigation Blockers: Fix issues that prevent users from reaching content
• Context Problems: Resolve areas where content doesn't make sense aurally
• Interaction Barriers: Address form and widget usability problems
• Structure Issues: Improve heading and landmark organization

Iterative Testing

• Test Early: Use simulation during development, not just at the end
• Regular Validation: Re-test after significant content or code changes
• Progressive Enhancement: Start with basic accessibility, then enhance
• User Feedback: Combine AI insights with real user testing

Integration with Manual Testing

Guided Manual Testing

• Focus Areas: Use AI insights to guide where to focus manual testing
• Test Scenarios: Create test scenarios based on identified issues
• Validation Points: Verify AI recommendations with actual screen readers
• User Journey Testing: Test complete user workflows, not just individual pages

The accessibility attribute analysis helps you understand how well your ARIA labels and descriptions serve users with assistive technologies.

Ready to learn about validating accessibility attributes? Continue to Accessibility Attributes.