Large language models (LLMs) possess strong capabilities in language understanding and generation, as well as remarkable problem-solving abilities. In the educational domain, a representative application is to employ LLMs as learning assistants that answer students’ questions and support their learning processes. In such scenarios, it is crucial for the model to perceive a student’s cognitive level and provide explanations that are appropriate to that level. However, whether LLMs can effectively accomplish this task has not yet been thoroughly investigated. To address this gap, we introduce CogBench, an evaluation benchmark designed to assess the cognitive alignment capabilities of LLMs in educational QA. CogBench comprises 2.1K mathematics questions, each associated with multiple valid solutions that rely on knowledge and reasoning at different cognitive levels. Building on this structure, we formulate three cognition-aware evaluation tasks and propose three complementary metrics to quantify cognitive alignment from multiple perspectives. Extensive experiments on 11 representative LLMs reveal that, while models can often produce correct answers, they still struggle to consistently generate explanations that are aligned with the intended cognitive level. These results highlight substantial room for improvement and establish CogBench as a diagnostic benchmark for advancing cognitively aligned educational AI systems.

“CCL2024-Eval任 务7为 中 小 学 生 作 文 流 畅 性 评 价 (Chinese Essay Fluency Evalua-tion,CEFE),该任务定义了三项重要且富有挑战性的问题,包括中小学作文病句类型识别、中小学作文病句改写、以及中小学作文流畅性评级。本队伍参加了评测任务7的三项子任务,分别获得了45.19、43.90和45.84的得分。本报告详细介绍本队伍在三个子任务上采用的技术方法,并对评测结果进行分析。”