mlsanityData to 7 Oct 2026

Classroom AI: Large Language Models as Grade-Specific Teachers

arXiv 2601.06225 ↗First posted 9 Jan 2026cs.CY cs.AI cs.CLcited by 2

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Abstract (arXiv)

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' comprehension capacities without sacrificing factual correctness. This approach integrates seven established readability metrics through a clustering method and builds a comprehensive dataset for grade-specific content generation. Evaluations across multiple datasets with 208 human participants demonstrate substantial improvements in grade-level alignment, achieving a 35.64 percentage point increase compared to prompt-based methods while maintaining response accuracy. AI-assisted learning tailored to different grade levels has the potential to advance educational engagement and equity.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 63 references that are in the map. First posted on arXiv 9 Jan 2026.

Builds on

31 of 63 references.

References · 31

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19 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026A Multi-Agent Approach to Validate and Refine LLM-Generated Personalized Math Problemscited by 0
  2. 2026Language Models for Portuguese: A Systematic Mapping Studycited by 0