Enhancing Accessibility Through AI and Machine Learning

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Technology & Software

Enhancing Accessibility Through AI and Machine Learning

NLP models interpret and simplify complex text to improve comprehension for all users.

Focus Areas

AI Accessibility Solutions

ML Inclusive User Experiences

Real-Time Personalization

Illustration showcasing AI-powered accessibility features such as real-time transcription, voice commands, and screen readers.

Business Problem

A global digital media and education company was committed to improving accessibility for users with visual, auditory, and cognitive impairments. However, its existing platforms lacked scalable assistive features and suffered from inconsistent user experiences across devices. Regulatory compliance with WCAG and ADA standards was a growing concern, especially as digital content expanded. Leadership aimed to embed AI and ML into the core product architecture to enable real-time personalization, assistive automation, and inclusive design—at scale.

Key challenges:

  • Inaccessible Content: Static UI components and non-descriptive media hindered usability for screen readers and assistive tools.

  • Manual Remediation: Accessibility audits and fixes were reactive, time-consuming, and inconsistent.

  • Low Personalization: The platform lacked real-time adaptation to diverse user needs and preferences.

  • Compliance Risk: The firm faced growing exposure to ADA and WCAG violations across global markets.

  • Lack of Analytics: No visibility into how users with disabilities engaged with content or navigated challenges.

The Approach

Curate partnered with the organization to design and implement AI- and ML-driven accessibility enhancements. The solution aimed to automate compliance, personalize user experiences, and embed inclusive design practices into the software development lifecycle.

Key components of the solution:

Discovery and Requirements Gathering:

  • Accessibility Audit: Evaluated web and mobile platforms against WCAG 2.1 AA standards.

  • User Research: Engaged users with disabilities to uncover pain points, usage patterns, and unmet needs.

  • Tech Stack Review: Assessed existing capabilities in computer vision, NLP, and UI rendering engines.

  • Stakeholder Workshops: Brought together product, design, legal, and engineering leaders to define a roadmap.

AI-Driven Accessibility Layer:

  • Developed a real-time accessibility engine using computer vision (CV) and natural language processing (NLP).

  • Enabled on-the-fly image captioning, video transcription, and dynamic UI adjustments.

  • Integrated with React and Flutter apps to auto-generate ARIA tags, landmarks, and focus states.

Machine Learning for Personalization:

  • Trained ML models on anonymized behavioral data to predict and adapt content formats (e.g., text-to-speech, font scaling, contrast adjustment).

  • Created customizable user profiles that dynamically adjusted interface components and navigation flows.

  • Used reinforcement learning to refine assistive recommendations based on interaction outcomes.

Automated Accessibility Testing and CI/CD Integration:

  • Deployed tools like axe-core and Google Lighthouse into the CI pipeline for continuous WCAG compliance validation.

  • Built a regression testing suite to detect accessibility breaks on UI changes.

  • Delivered real-time dashboards for compliance monitoring and release gating.

Inclusive Design Framework:

  • Created an internal accessibility design system with reusable components and guidelines.

  • Embedded accessibility reviews into pull requests and sprint planning.

  • Conducted training for designers and developers on empathetic UX and inclusive design principles.

Business Outcomes

Increased User Inclusivity


Users with visual, auditory, and cognitive impairments experienced improved navigation, content engagement, and satisfaction.

Automated and Scalable Compliance


AI and ML reduced manual testing effort and ensured consistent compliance across updates and platforms.

Real-Time Adaptation


The platform could now respond in real-time to individual user needs, improving retention and usability.

Brand Reputation and Market Expansion


Demonstrated leadership in digital accessibility, enabling entry into education, government, and enterprise markets with accessibility mandates.

Sample KPIs

Here’s a quick summary of the kinds of KPI’s and goals teams were working towards**:

Metric Before After Improvement
Accessibility defects/release 18 2 89% reduction
Time to resolve accessibility bugs 6 days 2 hours 96% faster
WCAG 2.1 compliance score 72% 98% 26% improvement
Visually impaired user engagement Baseline 48% Increase
Accessible content coverage 40% 100% Complete coverage
**Disclaimer: The set of KPI’s are for illustration only and do not reference any specific client data or actual results – they have been modified and anonymized to protect confidentiality and avoid disclosing client data.

Customer Value

Compliance by Design


Embedded accessibility into SDLC and product lifecycle.

Scalable Solution


Machine learning models enabled consistent, adaptive accessibility across platforms and user contexts.

Sample Skills of Resources

  • AI/ML Engineers: Built and trained accessibility models using CV and NLP.

  • Front-End Developers: Integrated adaptive UI components and dynamic rendering logic.

  • Accessibility Specialists: Conducted audits, testing, and remediation aligned to WCAG/ADA.

  • UX Designers: Crafted inclusive experiences and contributed to design system updates.

  • DevOps Engineers: Automated testing and integrated compliance gates into CI/CD pipelines.

Tools & Technologies

  • AI/ML: TensorFlow, PyTorch, spaCy, OpenCV

  • Accessibility Tools: axe-core, Google Lighthouse, WAVE, VoiceOver, NVDA

  • Front-End Frameworks: React, Flutter, ARIA standards

  • CI/CD & Testing: GitHub Actions, Jenkins, Cypress, Selenium

  • Monitoring & Analytics: GA4 Accessibility Events, Custom Dashboards, Segment

machine learning models processing user data to personalize accessibility features for diverse user needs.

Conclusion

By integrating AI and machine learning into its accessibility strategy, the organization fundamentally transformed its digital experience into an inclusive, intelligent, and adaptive platform. Curate’s approach blended innovation with compliance, empowering the firm to serve all users equally—and to do so at scale, with precision and empathy.

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