The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty
arXiv:2607.26067v1 Announce Type: new Abstract: 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
Record details
Published: 30 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Safety & alignment · Children & education · Finance, VC & PE
Retrieved: 30 July 2026
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ethics.ai (30 July 2026), “The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty,” evidence record 14512, https://ethics.ai/record/14512 (originally published by arXiv cs.CY).
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