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

EduAdapt is a benchmark dataset of nearly 48k grade-labeled question-answer pairs spanning Grades 1-12 across nine science subjects, designed to evaluate whether LLMs can adapt their responses to different K-12 grade levels with age-appropriate vocabulary and explanations. The benchmark evaluates both multiple-choice and open-ended questions aligned to Next Generation Science Standards and includes publicly available code and datasets.

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 learne

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