> ## Documentation Index
> Fetch the complete documentation index at: https://docs.learningcommons.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Overview of Student-Facing Text evaluators that score qualitative text complexity, from sentence structure and vocabulary to grade-level fit.

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## The problem

[Research](https://achievethecore.org/content/upload/Research%20Supporting%20the%20ELA%20Standards%20and%20Shifts%20Final.pdf) ↗ shows that students who consistently engage with complex texts are more likely to succeed in college and beyond. Yet despite their importance, complex texts often remain absent from classrooms.

* Quantitative measures of text complexity (e.g., Lexile or Flesch-Kincaid) are useful, but limited
* Qualitative measures are more accurate, but also more labor-intensive to assess

As AI-generated texts enter the classroom, **educators risk using content that looks grade-appropriate on the surface, but fails to meet the deeper demands of literacy development.**

## What we're building

Instead of giving a single complexity score, our Student-Facing Text evaluators assess text across multiple qualitative dimensions.

They are anchored in Student Achievement Partners (SAP)'s [Qualitative Text Complexity Rubric for Informational Text](https://learnwithsap.b-cdn.net/app/uploads/2026/04/Qualitative-Text-Complexity-Rubric-Informational.pdf) ↗, giving you:

* Fine-grained data to ensure quality generated texts
* Actionable insights into why a text may be complex or not complex enough and how to best scaffold it for students

| Evaluator                                                                                                                                       | <Tooltip tip="Student Achievement Partners">SAP</Tooltip> rubric dimension | Description                                                                                                                                       |
| :---------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------ |
| **[Grade Level Appropriateness](/evaluators/student-facing-text-evaluators/grade-level-appropriateness)<br /><EarlyAccessBadge size="xs" />**   | All dimensions                                                             | Determines whether AI-generated text is suitable for a grade band and suggests scaffolding that can support instruction of the text               |
| **[Background Knowledge Demands](/evaluators/student-facing-text-evaluators/background-knowledge-demands)<br /><EarlyAccessBadge size="xs" />** | Subject Matter Knowledge                                                   | Identifies the background knowledge a student needs to comprehend the generated text                                                              |
| **[Vocabulary Complexity](/evaluators/student-facing-text-evaluators/vocabulary-complexity)<br /><EarlyAccessBadge size="xs" />**               | Vocabulary                                                                 | Measures how challenging students may find the vocabulary of AI-generated texts                                                                   |
| **[Sentence Structure](/evaluators/student-facing-text-evaluators/sentence-structure)<br /><EarlyAccessBadge size="xs" />**                     | Sentence Structure                                                         | Assesses the complexity of sentence structure in informational texts relative to a specified grade level                                          |
| **[Meaning Directness](/evaluators/student-facing-text-evaluators/meaning-directness)<br /><EarlyAccessBadge size="xs" />**                     | Conventionality                                                            | Analyzes how directly a text communicates its meaning                                                                                             |
| **[Purpose Clarity](/evaluators/student-facing-text-evaluators/purpose-clarity)<br /><EarlyAccessBadge size="xs" />**                           | Purpose                                                                    | Assesses how clearly a text communicates its central purpose, and identifies elements that make that purpose accessible or challenging to readers |
| **[Organizational Structure](/evaluators/student-facing-text-evaluators/organizational-structure)<br /><EarlyAccessBadge size="xs" />**         | Organization                                                               | Assesses how demanding a text's organizational structure is for students at a given grade level                                                   |
| **[Reference Knowledge Demands](/evaluators/student-facing-text-evaluators/reference-knowledge-demands)<br /><EarlyAccessBadge size="xs" />**   | Intertextuality                                                            | Assesses what a text assumes you've already encountered and then builds on to create meaning                                                      |

Explore our [Student-Facing Text dataset](/evaluators/dataset/student-facing-text) for the benchmark data that our Student-Facing Text evaluators use to assess text complexity.

## Scope and limitations

<Warning>
  Student-Facing Text evaluator outputs should not be used for high-stakes
  applications like grading, assessment, or placement decisions without human
  review.
</Warning>

Remember that LLM scores can vary across runs, especially on borderline cases.
We recommend keeping a human in the loop and treating outputs as directional
signals vs. definitive judgments.

## Related topics

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