Recent research indicates that while large language models (LLMs) can predict item difficulty levels with moderate accuracy, they struggle with identifying truly hard items. Studies found that LLMs tend to underestimate the difficulty of items that challenge learners due to misconceptions, a phenomenon termed the "Easy Trap." This suggests that current LLMs may approximate curricular difficulty rather than cognitive difficulty, posing a risk of bias in educational assessment and adaptive systems. AI
IMPACT LLMs may introduce bias in educational assessments due to their inability to accurately gauge learner-driven difficulty.
RANK_REASON Two academic papers published on arXiv discussing LLM performance in educational assessment.
- arXiv
- Classical test theory
- Hugging Face
- Indonesian undergraduates
- item response theory
- Large language models
- The Easy Trap
- alphaXiv
- CatalyzeX
- Gotit.pub
- GPT-4.1
- GPT-5.4
- Indonesian
- LLMs
- ScienceCast
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