Check readability for a target audience by grade level

This checker takes a computed grade level and a target audience, then compares that number with a clear expected range. It tells you whether the text matches, is too easy, or is too difficult. Use it after any grade-level calculator to turn an abstract score into a consistent editorial decision for public information, technical documentation, or material written for children.

Start with a computed grade level

This capability evaluates a grade level that you already have; it does not analyze raw prose or calculate a readability formula. Supply the numeric result from a tool such as Flesch-Kincaid, together with one supported audience identifier. Fractional values are welcome because readability formulas commonly produce results such as 7.4 rather than whole school grades. The checker preserves that value and compares it against inclusive lower and upper bounds. Children are expected to fit grades 1 through 5, general-public writing grades 6 through 8, and technical material grades 9 through 14. These bands are editorial targets, not claims about an individual reader's education or ability. A score inside the selected band returns a matching assessment. A lower score is marked too easy, while a higher score is marked too difficult. Keeping calculation and audience evaluation separate makes the decision reusable with different scoring systems, provided their output represents a conventional grade level. It also lets a workflow store the original score alongside the policy decision for later review.

Choose the audience that reflects the document's job

Select children when the material is intentionally written for young independent readers, such as classroom instructions, museum activities, or basic safety explanations. Select general_public for services, announcements, help pages, and consumer information that should be understandable without specialist training. Select technical when the reader is expected to know a professional vocabulary and tolerate denser sentence structures, as in engineering notes, API concepts, or research operations. The audience names are exact: children, general_public, and technical. An unfamiliar value produces an input error instead of guessing, because a silent fallback could approve content against the wrong standard. Audience choice should follow the document's purpose, not merely the identity of its author. A software company can publish general-public billing guidance and technical integration documentation, for example, and those pieces deserve different checks. Likewise, a low grade is not automatically better. Text that falls far below a technical range may omit necessary precision, while text above a children's range can prevent the intended readers from acting confidently.

Use the assessment as an editorial signal

The output includes the supplied grade, the selected audience, the exact expected range, a Boolean match, and a compact assessment. A matching result means the number is inside the policy band, including either boundary. Too difficult means editors should look for long sentences, nested clauses, uncommon words, or unexplained terminology before scoring the revision again. Too easy means the text is below the selected band; for technical work, check whether simplification removed distinctions, constraints, or domain terms that readers need. The result is deliberately deterministic: identical inputs always produce identical outputs, with no model judgment, network request, randomness, or changing reference data. That makes it suitable for publishing gates, content-management checks, and repeatable audits. Still, grade level measures surface difficulty rather than factual accuracy, tone, accessibility, or cultural suitability. Treat a match as one piece of evidence, not final approval. Human review remains important for high-impact instructions and for children. When automating, retain the score and assessment so reviewers can see why an item passed or was flagged and can apply a documented exception when necessary.