Case Study
AI-Accelerated Accessibility Remediation for a University’s Digital Courseware
Key Result Highlights
- Identified and remediated more than 1,000 accessibility issues.
- Automated workflows resolved 55% of issues, cutting manual cycles in half.
- Zero disruption to live course operations with platform-native execution.
- Manual effort focused only on the most complex 45% to ensure quality and precision.
- AI-human hybrid model delivered fast, accurate, and scalable remediation aligned to ADA Title II and WCAG 2.2 AA.
The Client
The client is a leading higher-education institution focused on inclusive digital learning. As part of a broader modernization effort, the client aimed to bring its online courseware in D2L up to ADA Title II and WCAG 2.2 AA standards, ensuring full accessibility for all learners.
The Challenge
The institution needed a comprehensive audit and remediation of course templates and content within D2L. The work had to maintain instructional integrity, achieve high remediation accuracy, and avoid any disruption to ongoing course delivery.
Critical Success Parameters
- Execute platform-native remediation to keep live courses uninterrupted.
- Use AI to triage and resolve common failures, accelerating throughput.
- Reduce testing cycles through intelligent prioritization of shared components.
- Maintain alignment through bi-weekly stakeholder reviews.
- Preserve full traceability for compliance audits.
Our Approach
- Conducted a phased WCAG 2.2 audit of D2L templates, applying AI-driven fixes to improve HTML structure, semantics, and accessible design patterns.
- Resolved 55 percent of WCAG issues using automation to cut manual effort and accelerate turnaround times.
- Delivered all fixes directly within D2L, eliminating the need for reuploads, TOC remapping, or smoke-testing cycles.
- Provided a consolidated report combining automated findings with manual evaluations for complete visibility.
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