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📄 Research Article

The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty

● high · 2026-07-30llm, formative-assessment, adaptive-learning, feedback-loop, student-experience, stem-education, higher-ed, personalized-learning, ai-literacy, teacher-role

LLMs systematically underestimate the difficulty of misconception-driven items ('The Easy Trap'). While LLM ratings show moderate rank correlation with empirical student difficulty (rho=0.52-0.70), they misclassify several fraction items as easy that are among the hardest for students (e.g., 34% correct). LLMs approximate curricular rather than cognitive difficulty.

Relevance to AI in Education: This paper contributes to the understanding of Automated Assessment, Personalized Learning, and Student Experience. The findings have implications for Adaptive Learning systems, Formative Assessment design, and the broader Edtech Platform landscape. Future work should explore how these results generalize across STEM Education and Higher Ed contexts.

This research connects to the growing body of work on AI Literacy and Teacher Role, highlighting both the promise and limitations of AI tools in educational settings.

Connected Concepts

  • Automated Assessment
  • Personalized Learning
  • Student Experience
  • Adaptive Learning
  • Formative Assessment
  • Edtech Platform
  • STEM Education
  • Higher Ed
  • AI Literacy
  • Teacher Role

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    • Citation

    Amanda La Hadi, Muhammad Johan Alibasa, Guanliang Chen, A. Taufiq Asyhari (2026). The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty. arXiv:2607.26067. EDM 2026 (Educational Data Mining Conference).