<p>Although large language models (LLMs) demonstrate significant potential for advancing personalized science education, they face challenges in generating science problem-solving processes adapted to students’ grade levels. In this paper, we developed a Chinese Science Question (CSQ) dataset, which comprises both a benchmark and a training set, aiming to evaluate and enhance the science problem-solving capabilities of LLMs. The CSQ consists of 12,000 high-quality samples featuring a variety of question types and diverse discipline properties, covering four subjects and multiple topics at the Chinese primary school. We further designed the language model to reflect these discipline properties in the generated responses, emulating the thought process of students when solving science questions. We demonstrated that CSQ and its extensive annotations can be employed for fine-tuning models. This was confirmed through both automatic and human evaluations, particularly in generating problem-solving processes that are aligned with students’ grade levels.</p>