Classroom AI: Large Language Models as Grade-Specific Teachers

This paper presents a framework for finetuning LLMs to generate grade-appropriate educational content across six grade levels (lower elementary to adult), evaluated through readability metrics and human studies with 208 participants. The work addresses the challenge of making AI tutoring systems produce content matched to students' comprehension levels across K-12 grades.

Large Language Models (LLMs) offer a promising solution to complement traditional teaching and address global teacher shortages that affect hundreds of millions of children, but they fail to provide grade-appropriate responses for students at different educational levels. We introduce a framework for finetuning LLMs to generate age-appropriate educational content across six grade levels, from lower elementary to adult education. Our framework successfully adapts explanations to match students'co

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