LLMs常误判学生易错题，因它们只看教学顺序而非认知困难。

Large language models (LLMs) are increasingly used for estimating item difficulty in educational assessment. However, it remains unclear whether such estimates reflect how learners actually experience difficulty. This study investigates the alignment between LLM-generated difficulty ratings and empirical student performance on basic mathematics tasks. Four widely used LLM-based systems generated difficulty ratings on a 1-100 scale for 32 arithmetic items across multiple runs (N = 640 ratings). These were compared with empirical difficulty derived from responses of 770 Indonesian undergraduates using Classical Test Theory (CTT) and Item Response Theory (2PL). Results show moderate rank correlations (Spearman's rho = 0.52-0.70), indicating that LLMs capture coarse ordering of item difficulty. However, substantial and systematic misalignment emerges in fraction items. Several items consistently rated as easy by LLMs were among the most difficult for students, such as an item with only 34.16% correct for 100 : 1/2. We argue that LLMs approximate curricular difficulty, or what should be easy based on instructional sequencing, rather than cognitive difficulty driven by learner misconceptions. This leads to systematic underestimation of misconception-driven items, a phenomenon we term the Easy Trap. These findings highlight a critical limitation of LLM-based difficulty estimation and suggest that relying on such estimates without empirical grounding may introduce bias in assessment design and adaptive systems.