EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs

TL;DR

EduAdapt introduces a large-scale benchmark dataset of grade-labeled QA pairs across K-12 science subjects to evaluate and improve the ability of large language models to generate age-appropriate educational responses.

Contribution

This work provides the first dataset and evaluation framework specifically designed to assess grade-level adaptability in large language models for educational purposes.

Findings

Larger models perform better on grade-level tasks.

Models struggle with early-grade (Grades 1-5) response suitability.

EduAdapt enables development of more developmentally aligned educational AI.

Abstract

Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. Despite strong performance on academic benchmarks, they often fail to tailor responses to students' grade levels. This is a critical need in K-12 education, where age-appropriate vocabulary and explanation are essential for effective learning. Existing models frequently produce outputs that are too advanced or vague for younger learners, and there are no standardized benchmarks to evaluate their ability to adjust across cognitive and developmental stages. To address this gap, we introduce EduAdapt, a benchmark of nearly 48k grade-labeled QA pairs across nine science subjects, spanning Grades 1-12 and grouped into four grade levels. We evaluate a diverse set of open-source LLMs on EduAdapt and find that while larger models generally perform better, they still struggle with generating suitable responses for early-grade students (Grades 1-5). Our work presents the first dataset and evaluation framework for assessing grade-level adaptability in LLMs, aiming to foster more developmentally aligned educational AI systems through better training and prompting strategies. EduAdapt code and datasets are publicly available at https://github.com/NaumanNaeem/EduAdapt.