Your LMS Is Only as Accessible as Its Content: Where CIOs Should Start with AI‑Assisted Accessibility

For years, the LMS carried most of the blame or praise for accessibility. If a vendor’s documentation showed WCAG alignment, the assumption was that everything downstream was covered. But internal audits tend to tell a different story. The platform itself rarely causes the real friction. The trouble lies in the files, recordings, and slides that flow into it semester after semester. This volume of instructional material grows faster than any central team can realistically monitor.

Recent ADA Title II updates have made that distinction more consequential. What once felt like a design preference has shifted into a clear institutional obligation. The accessibility of course materials directly shapes whether a student can participate at all. And as expectations tighten, higher-ed technology leaders are finding that the real work begins with the content ecosystem the LMS  carries.

The New Accessibility Expectations Shaping LMS Strategy

Digital‑learning standards have moved closer to compliance territory. Technology leaders need a clear view of the rules that increasingly determine what counts as accessible course content.

WCAG: Still the Anchor Standard

WCAG defines testable success criteria across perceivable, operable, understandable, and robust content. The WCAG 2.2 update sharpens expectations around focus visibility, target size, consistent help, and authenticated experiences. These updates are influencing preparation for reviews, even when formal requirements still reference WCAG 2.0 or 2.1.

ADA Title II: A Clearer Mandate for Public Institutions

In April 2024, the Department of Justice finalized the new ADA Title II digital accessibility rule for state and local governments, including public colleges and universities. It establishes WCAG 2.1 Level AA as the technical standard and sets phased deadlines that push most institutions toward meaningful compliance by April 2027. The rule reaches deeply into the academic technology stack and, critically, the course content delivered through the LMS.

508, EN 301 549, and the Broader Compliance Landscape

Beyond Title II, other frameworks continue to shape expectations. Section 508, which applies to federal agencies, aligns with WCAG and remains a reference point for institutions operating in federally funded environments. Meanwhile, Europe’s EN 301 549 standards effectively embeds WCAG 2.1 AA into ICT requirements. Making it increasingly relevant for institutions with global programs, cross‑border partnerships, or internationally sourced tools.

Taken together, these regulations set the terms higher ed institutions must operate within. What comes next is understanding how those expectations translate into everyday teaching materials, because that is where most institutions discover the gap between policy and practice.

Where Accessibility Breaks Down: The LMS Shell vs. the Course Content

The disconnect becomes clearer when institutions shift from reviewing LMS features to examining the materials that fill each course shell. Most LMS platforms have steadily improved on the basics, and vendors typically meet their stated conformance levels. The fragility shows up in the instructional content faculty upload every term, because that content varies widely and is rarely created with accessibility standards in mind.

How the gap typically appears:

These issues appear across disciplines but are especially visible in STEM courses make them more visible. Equations, diagrams, and complex tables often come in formats that assistive technologies cannot read, even when the LMS itself is accessible. Examples include:

• Equations saved as images, which fail the non-text Content (1.1.1).
• Diagrams without meaningful descriptions, which fall short of Info and Relationships (1.3.1).
• Complex tables without headers or scope, which conflict with table structure requirements under 1.3.1.

At scale, these kinds of content gaps are difficult for any institution to manage. The pace of manual review can’t keep up with the flow of files through the LMS. That reality is what has pushed AI into the accessibility conversation.  Making it a way to handle the sheer operational load that human teams cannot absorb alone.

What AI Can Solve in Accessibility, and What Still Requires Human Judgment

AI can play a practical role in accessibility work. The question for higher‑ed technology leaders is where it adds real value and where it still needs human oversight.

Where AI Helps

• Discovery and Inventory: Scan LMS course shells, locate files, and flag obvious issues such as missing alt text, missing captions, and untagged PDFs or images that contain text, tables, or equations.
• First‑pass Remediation: Draft alt text, generate initial captions and transcripts, and extract basic structure from PDFs and tables.
• Triage and Prioritization: Rank issues by severity and impact to group findings into manageable queues.

Where AI Falls Short

• Accuracy and Context: It cannot judge whether a description is correct, meaningful, or appropriate for the course context.
• Caption Quality: It struggles with domain‑specific terminology, accented speech, and poor audio quality, preventing captions from meeting WCAG expectations.
• Pedagogical Interpretation: It cannot determine when a formula, diagram, or complex table needs a long description, an alternative format, or human re‑authoring.

Because of these limits, AI works best in a human‑in‑the‑loop model. AI handles the volume while humans ensure accuracy and compliance. Magic EdTech’s overview of accessibility testing explains where automation can support the process and where human review is still essential. Any tool promising full WCAG or ADA compliance without human review introduces more risk than it removes.

The Core Steps CIOs Need to Operationalize AI‑Assisted Accessibility

AI supports accessibility work only when it operates within a defined structure. These six steps provide a workable foundation.

1. Set the Standard You Will Follow

Treat WCAG 2.1 AA as the baseline under ADA Title II, adopt WCAG 2.2 where practical, and align to EN 301 549 for global programs.

2. Build a Current Inventory of LMS Content

Use LMS APIs and AI‑assisted discovery to map everything inside active and inherited course shells. Treat the inventory as a living catalog, not a one‑time audit.

3. Run Automated and AI‑Enhanced Checks

Pair standards‑based testing with AI models that detect untagged PDFs, images containing text or math, and videos lacking usable captions. Focus on broad coverage, not perfection.

4. Prioritize What Gets Remediated First

Rank issues by severity and course impact. High‑enrollment and required courses typically come first; fix blockers (such as missing captions or unreadable PDFs) before minor issues.

5. Define Where Humans Must Remain in the Loop

Allow AI to draft alt text, MathML, or captions, but ensure faculty or accessibility specialists validate accuracy and context. Sensitive visuals and STEM content still require human interpretation.

6. Protect Data, IP, and Student Information

Avoid sending course materials to public AI tools. Use secure environments and clear data agreements, especially since ADA Title II holds institutions responsible for the accessibility of the tools they provide.

Taken together, these steps provide CIOs with workable frameworks for scaling accessibility without overextending their teams. AI accelerates the process, but it’s the institution’s policies, people, and safeguards that make it sustainable.

Moving Toward Sustainable Accessibility

Recent enforcement activity makes the stakes clear. The DOJ’s final ADA Title II rule requires public colleges and universities to meet WCAG 2.1 AA for web and mobile content, with compliance deadlines beginning in 2026–2027. For most institutions, the challenge is not the LMS itself but the volume of course materials that now fall under this rule.

In one higher-ed remediation project, Magic EdTech helped a university address more than 1,000 accessibility issues, with AI speeding up roughly half the fixes and human reviewers completing the rest. The outcome reflects a broader reality: institutions need scalable methods to bring thousands of learning materials up to standard.

As the compliance window narrows, the institutions that succeed will be those that blend AI efficiency with informed human judgment, creating a sustainable model for accessible digital learning rather than a one-time fix.