Checking the Factual Accuracy of AI Infographics before Publication

A beautiful infographic can have every number in the right place while its bar lengths do not match the values. Or a diagram can show the required stages while an arrow creates a causal relationship the source does not support. Checking the text does not cover these problems. Learning materials require comparison of the data, wording, and visual encoding. Readers draw conclusions from the whole image, so factual accuracy must include the messages conveyed by shape, scale, and composition as well as the correctness of sentences.

AI can help suggest a structure, design, or list of potential weaknesses. Its output nevertheless remains material to check. NIST GenAI Profile describes the risks of inaccurate generative content. For infographics, practical checking begins with an independent source foundation that the generator did not create itself. All numerical examples below are invented to explain the procedure. They do not describe results from a real course, product, or study we conducted of a particular model's accuracy.

Prepare the Source Dataset before Designing

Save the table, indicator definitions, and sources before creating the image. If there are no numbers, do not ask AI to invent plausible statistics to make the design look finished. For demonstrations, use a clearly labeled hypothetical dataset. Real materials require a factual basis. An infographic should represent verified content, rather than force the author to search later for sources supporting attractive claims that happened to appear in the finished layout.

For each value, specify the object, period, unit, and counting method. A percentage without a denominator often leaves it unclear what is shown. A duration without a starting point may refer to a different process. If a parameter is absent from the source, leave a gap. Do not fill it with an assumption simply for design symmetry. Simplify the message or obtain the necessary clarification before preparing the graphic.

Save the specific dataset version and the date it was checked. If the source is updated, the team should know which data it used for this infographic. A link to an institution's homepage is insufficient. You need a path to the decisive table or passage. This simplifies repeat checking, reveals changes, and helps distinguish a layout error from a source update that occurred after the material was created.

List Every Message the Image Conveys

Write down the title, labels, numbers, legend, and textual conclusions. Add the meaning conveyed by arrows, areas, order, and sizes. An arrow, for example, may mean sequence, dependence, or causation. Readers should not have to guess which meaning you intended. Name it and check its basis. A visual element then becomes a part of the factual message that can be compared with the source, rather than an invisible decoration.

Assign each message a status: fact, interpretation, recommendation, or hypothetical example. Do not combine them in a single strong headline. Data may support a difference without supporting its cause. An example may illustrate a principle without being real statistics. This analysis helps check whether the design has strengthened the conclusion. Review large, short phrases especially carefully: readers will see them before small explanations and source notes.

Keep only the messages needed for the learning objective. If an infographic tries to explain everything, source limitations can disappear among the details. Choose the main question and sufficient data. There is no need to make materials more spectacular by adding unverified numbers. A missing element can be replaced with more precise wording or a different form of presentation that matches the factual foundation and enables the student to perform the intended action correctly.

Check the Arithmetic Independently

Repeat totals, proportions, and conversions using an ordinary calculation or an appropriate tool. Do not use the text in the picture as the input for the check: it may already be wrong. Return to the source dataset. If an assistant provides a formula, check its input values. A correct arithmetic operation with the wrong denominator produces a convincing number that still does not answer the question or represent the specified group.

Check rounding and explain it when it affects the total. Do not randomly change an individual value just to obtain a neat sum. If categories are not parts of one whole or may overlap, do not force them to add up to one hundred percent. The representation must match the definition. A useful learning exercise gives students a dataset where this distinction determines whether a pie composition is appropriate or another form is required.

Check the sign and direction of change. A higher result does not always mean a better one when the indicator represents errors or duration. Color or an arrow may automatically suggest the opposite. Check the indicator's meaning, rather than just numerical order. Likewise, an absolute difference and a relative change answer different questions. Do not let a short label mix them for a simpler visual message.

Align Units and Notation

Check every unit and which value it belongs to. Seconds and minutes require the appropriate conversion, while percentages need a clear base. For technical materials, use proper notation; SI Brochure BIPM is a primary source on the International System of Units. Do not try to infer a unit from a number's appearance or the illustration's topic if it is unspecified in the source dataset and has not been independently established.

Check that units agree between the label and the scale. If a title refers to minutes while the axis remains in seconds after editing, the material will mislead even with correct figures. As a check, describe the quantity in a complete sentence. This helps reveal a missing parameter. After shortening a label, check that the value remains unambiguous to a reader without access to your original notes or layout history.

Do not replace complex notation with arbitrary symbols for decoration. If the generator distorts a letter, subscript, or sign, correct it in a controlled, editable element. In a subject diagram, this detail can change the entire conclusion. Do not trust a formula's realistic appearance. Check it with a specialist or against an appropriate foundation if it lies beyond your expertise and determines the learning material's meaning.

Compare Geometry with the Numbers

Bars, sectors, areas, and repeated icons convey quantitative values. If they look arbitrary, a correct label does not repair the false impression. For numerical content, use controlled construction from data and reserve generative illustration for appropriate decorative elements. These are different tasks. Do not ask a raster scene to ensure precise proportions on its own merely because it broadly resembles a professional statistical chart.

Check the relationships between elements. If a value is twice as large, the corresponding bar length should convey exactly that on the chosen scale. Area and volume behave differently, so decorative enlargement of an icon can exaggerate a difference. Choose a simpler representation that is easy to check. Its educational value lies in correctly distinguishing quantities, rather than in the complexity of a three-dimensional composition that creates an ambiguous visual impression.

Compare the final result with a simple reference chart or table. If the picture tells a different story from the numbers, revise the design. Do not change the data to fit the desired composition. In a pilot, you can remove labels and ask someone to estimate order or proportions, then restore the numbers. A discrepancy reveals where visual encoding outweighs the text and needs substantive correction.

Check the Axis, Origin, and Intervals

For bar comparisons, the axis origin strongly affects how lengths are perceived. The ONS guidance on bar charts explains the relevant presentation rules. Do not mechanically apply one rule to every chart type. You need to know exactly what length, position, or area encodes in your chosen format. If the desired emphasis requires distorting proportions, consider another suitable data representation with a clear scale.

Check that intervals are equal and tick marks correspond correctly. A generative picture can draw a plausible axis with the wrong order or uneven spacing. Correct it using the data. For a time sequence, establish whether intervals actually are equal. Do not connect cases as though observations were continuous if the source does not support this. Visual smoothness must not conceal gaps or changes in measurement methods between points.

Keep a clear title and the necessary context. Readers should know the object, period, and unit without searching through a tiny footer. If the format is too cramped, reduce the number of messages or change the layout. Do not remove defining information just for a cleaner look. A label should help students read the numbers correctly, rather than make them reconstruct hidden conditions from colors or a familiar lesson topic.

A Hypothetical Example with Three Stages

Imagine an invented learning process with three stages lasting four, six, and ten minutes. The total is twenty minutes, and the shares are twenty, thirty, and fifty percent. This dataset explains the checking process; it is not an actual course measurement. If the infographic shows three equal segments, it does not convey the specified proportions. Correct numbers below do not eliminate a visual contradiction readers may perceive before the text.

Also check whether stages really are sequential and do not overlap. This is stipulated in our hypothetical dataset. A real process may differ, so simple addition requires an appropriate basis. The author must preserve this condition. If AI adds a loop arrow or parallel route, it changes the model. Confirm the new relationship or remove it, rather than leaving a decorative detail that contradicts the factual description of the process.

Give students the source values and several graphic alternatives. Ask them to identify the decisive problem and propose a correction. Assess their reasoning: an incorrect share, scale, label, or stage logic. Do not ask them to guess which version AI created. The learning objective is to check a visual message regardless of authorship. After correcting it, participants should explain how the new version now matches the numbers and limits of the hypothetical dataset.

Do Not Use Pictograms as Imprecise Statistics

Repeated icons can convey quantity, but readers must know their meaning. If one symbol represents several cases, explain this. If icons are merely decorative, do not arrange them to look like an exact count. Check automatically generated groups of people or objects particularly carefully. They can create a convincing sense of proportion without actually matching the dataset the author intended to show.

Do not replace part of a symbol with arbitrary cropping if that makes the value ambiguous. For a small dataset, a simple label or table may be more accurate. Design should aid comparison, rather than make students count decorative elements and guess their weight. If a pictogram contains a social or professional image, also check the appropriateness of its representation: it must not add unsupported characteristics to a group actually described only by numbers.

After reducing the image, check whether elements remain distinguishable. What was clear in a large layout may merge into a decorative patch. Do not force readers to zoom in for the decisive conclusion if the purpose and format call for quick reading. Keep accessible data alongside it. This allows the message to be checked and avoids making the visual form the only route to information needed for the learning task.

Check Causal and Spatial Models

In a diagram, an arrow pointing the wrong way can matter more than a language error. List every relationship and compare it with the source. If a cycle is shown, check the return condition. If layers are used, make sure they do not create a false hierarchy. A generator may add a convincing structure absent from the material. A complex subject model needs a responsible specialist able to assess its content, rather than merely the scene's overall impression.

Separate metaphor from precise representation. An abstract road may illustrate a learning path without being an actual model of time expenditure. If the difference matters, label it. Do not let an attractive analogy replace an explanation. Students must know which properties can transfer from the picture to the concept and where that correspondence ends. As an exercise, ask them to name one case in which the metaphor no longer works.

Do not use an infographic as the sole basis for a fact. It presents material rather than independently confirming it. Sources and definitions must remain available. If a new conclusion emerges after visual editing, check it separately. This prevents a team from carrying an accidental generative detail into learning content, where it may later appear to be an established rule merely because it has been repeated across slides and explanations.

Add Accessible Content and Check It

W3C WAI addresses complex images through sufficient text alternatives and explanations. An infographic needs to convey its main conclusion and necessary data, rather than merely list colors. Compare the accessible version with the graphic. If they describe different values, one needs correction. Do not treat automatically generated alt text as confirmation of an image's accuracy or a complete representation of every decisive relationship.

Check whether students can perform the intended task using the accessible content. If they need to compare stages, the relevant values and conditions must be available. A short decorative description is insufficient. At the same time, do not overload the alternative with unnecessary details that do not affect the goal. A simple material may need a brief explanation; a more complex one, a table or extended text. The decision depends on the image's informational function in the specific learning context.

Separately check the language and readability of every inscription in the final file. A subscript, sign, or letter may look different after export. If the text cannot be read reliably, the material is not ready for its intended use. Do not rely on the editor preview. What matters is the actual result users receive on their devices, together with the caption, alternative, and surrounding lesson explanation.

Organize an Independent Final Review

Give another person the source dataset and final infographic without a lengthy oral explanation. Ask them to check the decisive numbers, units, proportions, and conclusion. If they cannot reconstruct the basis, add the necessary reference point. A complex topic requires appropriate subject expertise. Do not replace it with a vote between several AI evaluations that may repeat the same mistake or assess only the plausibility of the visual design.

Keep a short list of corrections and recheck what they affect. Changing one label can disrupt the legend, while scaling can blur text. There is no need to restart the whole analysis without a reason, but decisive relationships must remain correct. For a pilot, save one illustrative error. It will help the next author understand the rule and avoid repeating the same faulty transition from correct data to an inaccurate visual form.

The WeStudy LMS platform can help organize materials, sources, and an educational analysis of infographics. Check the capabilities of a specific graphics tool or automated checking separately. A clear data table and a subject exercise are enough to begin. Students should show exactly what does not match the source and how a correction changes the message. This outcome matters more than the number of decorative flaws found that do not affect factual understanding.

Check That the Title Matches the Data Shown

A large headline can make a stronger claim than the infographic itself. Ask the reviewer to distinguish what the dataset directly shows from the author's interpretation. Showing the duration of three stages does not establish the best organization of the process or the effectiveness of learning. Such claims require other evidence. Narrow the title to the established content or add an appropriate explanation, rather than relying on a tiny note that formally limits a dramatic main phrase.

Also check the caption on the page, message, or slide where the file is used. It can change the interpretation of a correct graphic. The same hypothetical table must not suddenly become real statistics through new introductory text. Preserve its origin and limitations. Checking must cover the package readers actually receive, because the learning message emerges from the combination of image, title, surrounding explanation, and the question students must answer after viewing it.

Repeat the Check on One Decisive Conclusion

Before finishing, choose the most important message and trace it backwards: visual form, label, calculation, source value, and definition. If a transition is unsupported, correct or narrow the conclusion. This short review helps find a hidden problem lost among many correct details. It does not replace every check, but returns attention to why the infographic was included and what outcome readers should obtain from it.

After correction, save the reason for the change together with the data version. The next author can then reproduce the logic without guessing why the attractive original was rejected. This is useful learning material for the team: it shows a specific connection between the factual foundation and the design. Reviewers should see that the change solved the decisive problem, rather than merely making the picture visually calmer or more similar to other course materials.

Preserve the Connection through Publication Preparation

When the WeStudy LMS platform is part of the program, check the actual embedding: size, caption, accessible data, and source. This does not mean publishing automatically; first complete the checks required by your process. After a source changes, review the related elements. A well-prepared infographic lets readers draw the correct conclusion from the graphic and check it against the data. Visual persuasiveness should support this accuracy, rather than hide a gap in the evidence or an accidentally created new claim.

Sources

• NIST: Generative Artificial Intelligence Profile.

• Office for National Statistics: Simple comparisons — Bar chart.

• BIPM: The International System of Units — SI Brochure.

• W3C WAI: Complex Images.