Research Tool
Item Difficulty Estimator
Estimate assessment item difficulty using the AIED 2026 approach: LLM chain-of-thought analysis with anchored rubrics and bias correction. Based on our research across 200 experimental conditions and 15+ models.
Try an example:
Enter an item and click “Estimate Difficulty”
Or try one of the examples above
How it works
1. Chain-of-thought analysis
The LLM analyzes 7 difficulty factors before estimating: cognitive steps, prerequisites, misconceptions, transfer distance, working memory, distractor quality, and reading load.
2. Anchored rubric
Instead of asking “how hard is this?” we provide grade-level calibrated examples at each difficulty level. This grounds estimates in concrete comparisons.
3. Bias correction
LLMs exhibit variance collapse (clustering around 0.50) and systematic overconfidence. We apply corrections from our AIED 2026 research across 200 conditions.