CogBench: Benchmarking Cognitive Alignment of Large Language Models in Educational Question Answering

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. The code and dataset are released at https://github.com/kg-bnu/CogBench.