Digital Accessibility for Higher Education: A Scalable Remediation Model

Every accessibility engagement starts with the same uncomfortable math.

A university has hundreds (sometimes thousands) of courses sitting on a learning platform. Many were built years ago, before accessibility standards became compliance requirements. Meanwhile, students with disabilities rely on those same courses to work with assistive tools. And somewhere between the institutional mandate and the remediation work, there’s a gap that no spreadsheet, no vendor pitch, and no goodwill alone can close.

I’ve sat in on enough conversations to know that institutions know they should fix accessibility gaps. But the main question is: can this actually be done at scale, without the wheels coming off?

Through this blog, I will attempt to showcase how we helped a large online university tackle accessibility remediation across a high-volume course catalog without relying on a slow, fully manual process or waiting on a platform-native solution. We’ll go through how Magic EdTech used an AI-assisted workflow, backed by human review, to identify, remediate, and validate more than 1,000 accessibility issues across LMS-hosted courseware.

This recent engagement answered that question with more clarity than I expected. What it revealed was less about what AI can do in isolation and more about what becomes possible when AI and human expertise are designed to work in sequence.

A Real-World Higher Ed Accessibility Stress Test

One of our client institutions had a substantial catalog of courses. It was the kind of volume where a purely manual approach to accessibility audit and remediation would take years and a large team of people working in parallel.

The institution needed to move quickly and needed results they could trust. They were already being presented with an alternative solution by their current learning platform provider.

That last part matters. The platform provider proposed a native solution that would enable remediation within the platform itself. It was positioned as the path of least resistance.

Our accessibility team had to demonstrate that full accessibility remediation was worth choosing over a built-in option from a vendor they were already paying.

Ultimately, the platform provider’s solution never actually launched. Magic EdTech did.

The Accessibility Process We Built and Why It Worked

The workflow Magic deployed through our MagicA11y offering was an engineered system designed to model how accessibility violations actually behave in LMS-hosted courseware.

Here’s what that looked like in practice:

Automated scripts traversed each course page by page, executing accessibility checkpoints at every step. Wherever a violation was identified, the script captured the relevant code, sent it through an AI layer for remediation, and replaced the non-compliant code on the fly, all within Magic’s secure, controlled environment.

Pattern recognition across recurring accessibility violations, allowing AI-based automation to resolve high-frequency issues efficiently while flagging complex cases for human review.

Before any of this ran on live course content, Magic’s IT team reviewed the scripts and the execution environment together with the client’s IT stakeholders. Access was approved only after that review. Nothing ran on production without sign-off.

The result of the automated processing was both a remediated output and a detailed audit report, categorized by content type.

The team identified over 1,000 distinct accessibility issues across the course set, broken down by component: accordions, audio players, video elements, images, and HTML markup. Each violation was mapped to underlying HTML markup, ARIA roles and attributes, media player controls, and document accessibility structures, enabling precise remediation at the component and code level.

But here’s the part that separates this approach from pure automation: after every automated pass, the team ran a thorough human review. The purpose wasn’t to redo what automation had done. It was to validate it, catch anything the scripts had corrected imperfectly, and identify issues that automation alone couldn’t fully resolve.

That human layer is what makes MagicA11y’s approach distinct. The AI makes the work faster. The human review makes the output trustworthy.

A Lesson in Using AI for Accessibility Remediation

Our automated workflow didn’t perform perfectly on the first run. There’s an honest version of every project, and in this one, the team encountered a timing issue in the script: because the code replacement process didn’t always complete before the script advanced to the next page, some sections were being skipped. Content was being missed.

The team caught it, diagnosed it, and fixed it by building wait logic into each page transition so the replacement process was completed fully before moving on.

That’s the actual story of how a robust process gets built. The optimization came from running the work, seeing what broke, and fixing it.

By the end of the engagement, the scripts were measurably better than on day one, and those improvements carry forward into every subsequent engagement.

This is the thing about effort savings in accessibility work. They’re not static. Magic’s team ended this engagement having achieved 65% effort savings compared to a fully manual approach.

That figure also understates the longer-term value, because the process that produced it is now more capable than when it started.

The data bore this out in a pattern worth noting: timelines across the team, once normalized for course complexity and content type, were consistent. Complex courses with dense interactive elements simply take more effort than simpler ones.

When you account for that, all roles were operating within expected ranges. Rework decreased as engagement progressed, a reliable signal that the system is stabilizing.

What MagicA11y Does and What This Engagement Represents

MagicA11y is Magic’s AI + Human Review offering for digital accessibility. It spans a full range of content types: courseware, alt text generation, color contrast validation, video, images, documents, EPUB, PDF-to-EPUB conversion, and VPAT generation.

MagicA11y, built on decades of accessibility experience, is designed to handle the scale institutions actually face, working through entire catalogs rather than course by course.

This particular engagement was focused on LMS-hosted courseware remediation specifically. What made it work is the same thing that makes the broader MagicA11y framework work.

Automation handles high-volume, pattern-based work at speed, while human reviewers catch what automation misses and validate its output. Neither replaces the other. That combination is what produces results in quality, speed, and output confidence that a single-track approach cannot match.

The client’s leadership said as much. The project received appreciation from senior stakeholders, including at the director level. Not because the project was easy, but because the team handled its complexity transparently, delivered clear analysis, and helped the institution understand not just what was fixed, but what a path forward for their larger course catalog could look like.

What I’d Tell Any Accessibility Lead Starting This Conversation

After managing engagements like this one, a few things I’ve come to hold as true:

• Speed comes from system design. The biggest gains in this engagement didn’t come from people working harder. They came from building and refining a workflow that reduced the repetitive burden on skilled reviewers, freeing their time for work that required judgment.
• The quality of your audit report is the quality of your credibility. The categorized issue breakdown this team produced was, for the client, the most tangible proof that the work was being done with rigor. When leadership can see what was found and how it was addressed, they trust the remediation.
• Optimize honestly, report accurately. There’s a temptation in engagements like this to present the cleanest version of the numbers. The more useful practice is to understand what those numbers actually mean. Distinguishing between effort variance driven by course complexity and variance driven by execution gaps matters, especially when presenting to institutional leadership.
• The platform provider is not always the right answer. Institutions often assume that remediation tools built into their existing platform will be the lowest-friction path. In this case, the native option never moved past a proposal. The work still needed to get done, and it did, with a team that built a purpose-designed process around the institution’s actual content.

For universities and institutions with large course catalogs and unresolved accessibility obligations, the path forward doesn’t have to be as daunting as the backlog suggests. AI-assisted workflow, paired with skilled human review, has changed what’s possible, and it’s delivering real results in production environments right now.

If you’re thinking through what that looks like for your institution or your content portfolio, the Magic team is the right place to start. Learn more about MagicA11y and Magic EdTech’s accessibility services.