Reviewing AI-Generated Alt Text for Academic Figures

An AI-generated description of an academic figure can be fluent yet omit the information students need. It can also invent a value, process or relationship that the image does not show. Check the resulting description against the figure and its teaching context.

Illustrative failure modes

A chemistry diagram might receive “a diagram of molecular structures,” omitting the reaction the lesson addresses. A chart might receive “a line graph,” omitting variables, units and the relevant comparison. These examples are hypothetical, not reported customer files or measured model outcomes.

A longer description is not automatically better. Adding an unsupported reaction name or reversing an arrow can mislead the reader. Use the actual figure, caption and surrounding material to decide what the description needs to convey.

Presence is not accuracy

An automated checker may detect that alt text exists. That is not proof of conformance with the applicable non-text-content requirement, because the alternative must serve the relevant purpose. Incorrect text does not become conformant merely because a scanner returns green.

Inaccessible or misleading alternatives can create student-access barriers and legal risk. Whether a particular situation violates Section 504, Title II or another applicable law depends on the institution, facts and legal requirements. This article does not classify every generated error as a specific discrimination finding or predict an investigation outcome.

What the IFR says

The DOJ's April 2026 IFR discusses limitations on automated remediation at scale. Separately, its preamble recounts a Congressman's concern about STEM material and human oversight. That attributed comment informs scrutiny of claims but does not create a new prescribed review method.

Review with the right expertise

Use subject expertise to verify the teaching point and accessibility expertise to assess the student's route to that information. Faculty may supply important context; specialist review may be needed for complex descriptions and alternatives. Define suitable responsibilities rather than treating any role as universally sufficient or insufficient.

Compare generated text with the original figure, check invented details and decide whether a short description, surrounding explanation or longer equivalent alternative is appropriate. Do not mark an informative figure decorative merely to remove a warning.

Evaluate a workflow on academic content

Aelira Core v0.9.11 is beta. Supported workflows can detect issues, suggest changes and apply eligible partial fixes. Other findings need manual work, and some inputs may not produce a downloadable remediated artifact. Review the actual saved file, its recorded fixes and its unresolved findings. An automated score does not establish WCAG conformance.

Sample figures from the institution's disciplines and assess actual saved outputs. Record corrections, unresolved cases and reviewer decisions. Proposed contract metrics and liability terms should be negotiated for institutional needs; they are not mandated by the IFR. Retained evidence supports accountability, while the accuracy and usefulness of the published alternative remain the goal.

Further reading

These links provide additional context; each source should be assessed for its own scope.

AI-Assisted University Accessibility: Review, Evidence and Legal Scope

Resource 2: www.w3.org

How to Use OCR Resolution Agreements in Accessibility Planning