让大模型按不同年级水平讲题，提升教学适配性。
Classroom AI: Large Language Models as Grade-Specific Teachers
- 用七种可读性指标聚类，微调大模型生成分龄内容。
- 相比提示工程方法，年级匹配度提升35.64个百分点。
- 适合教育科技、个性化学习研究者参考。
大型语言模型（LLMs）为缓解全球教师短缺问题提供了潜力，但难以针对不同教育阶段的学生提供恰当的回应。本文提出一种微调框架，使LLM能在小学低年级至成人教育共六个年级层次上生成符合认知水平的教育内容。该方法通过聚类整合七种成熟可读性指标，并构建了用于分年级内容生成的综合数据集。在208名参与者的多数据集评估中，该方法显著提升了内容与学生年级的匹配度，相较基于提示的方法提升了35.64个百分点，同时保持回答准确性。面向不同年级的AI辅助学习有望促进教育参与度与公平性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。