Reference documentation for the Grade Level Appropriateness Evaluator.
Early access This functionality is actively evolving. Changes may occur as we expand capabilities and improve accuracy and reliability. Email support@learningcommons.org with your feedback or issues.
The Grade Level Appropriateness Evaluator assesses whether AI-generated text is suitable for independent reading at a specified grade band. The evaluator considers:
Flesch-Kincaid grade level
Word count
Text structure (complexity of organization, connections between ideas, role of text features etc.)
Language features (vocabulary, sentence complexity, use of figurative or abstract language)
Purpose (how explicitly stated and how concrete or abstract)
Knowledge demands (discipline-specific knowledge required, references, and allusions)
Student background knowledge (what a student at a given grade level would already know)
The grade band where a student can read the text independently, with a breakdown of why — quantitative score, qualitative features, and assumed background knowledge
Validate that your LLM prompts produce grade-appropriate content; aggregate reasoning across runs to diagnose and fix systemic complexity issues
Alternative grade + Scaffolding needed
A lower grade band where the text can still work with targeted support
Surface scaffolding suggestions (e.g., vocabulary pre-teaching, read-aloud) to help educators adapt content for mixed classrooms
Use grade and reasoning together to evaluate and improve the complexity of your AI-generated content. Use the alternative grade and scaffolding recommendation together to help educators adapt that content for a wider range of learners.
