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The problem

Research ↗ 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.

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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 ↗, 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
Explore our Student-Facing Text dataset for the benchmark data that our Student-Facing Text evaluators use to assess text complexity.

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Scope and limitations

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

Student-Facing Text dataset

Explore the expert-annotated benchmark behind Student-Facing Text evaluators.

SDK API Reference

Integrate Student-Facing Text evaluators into your TypeScript or Python project.

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