Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction. Existing methods often depend on costly human calibration or item-level textual representations, providing limited evidence about the cognitive processes that make items difficult. We argue that difficulty should be viewed not only as a property of item text, but also as an observable consequence of the problem-solving burden an item induces. Large Reasoning Models (LRMs) offer scalable process evidence through reasoning traces, but such evidence must be structured to support interpretable modeling. To this end, we introduce Epi2Diff (Episode to Difficulty), a framework that maps LRM reasoning traces into cognitively grounded episode sequences. These episodes group trace segments into functional problem-solving states, enabling difficulty to be modeled through reasoning scale, effort allocation, and state transitions. Epi2Diff extracts compact episode-dynamic features and combines them with semantic item representations for human difficulty prediction. Experiments on four real-world human difficulty datasets show that Epi2Diff consistently outperforms strong baselines, including fine-tuned small language models, LLM in-context learning, and supervised LLM adaptation. On SAT-derived classification benchmarks, Epi2Diff achieves an 8.1% average relative gain over supervised LLM fine-tuning baselines. Further analyses show that harder items induce more effortful, iterative, and implementation-centered episode dynamics, rather than merely longer responses. These results demonstrate that cognitive episodes in LRM reasoning traces provide a predictive and interpretable process representation for human item difficulty, offering a new lens for educational measurement with reasoning models.
Can LLMs Estimate Cognitive Complexity of Reading Comprehension Items?
Graduate School of Artificial Intelligence, POSTECH, Republic of Korea（韩国立科学技术院人工智能研究生院）；Department of Computer Science and Engineering, POSTECH, Republic of Korea（韩国立科学技术院计算机科学与工程系）
A Closed-Loop Personalized Learning Agent Integrating Neural Cognitive Diagnosis, Bounded-Ability Adaptive Testing, and LLM-Driven Feedback
Faculty of Artificial Intelligence in Education（人工智能教育学院）；Central China Normal University（中国中部师范大学）；School of Electrical and Electronic Engineering（电气与电子工程学院）；Hubei University of Technology（湖北工业大学）
Beyond Scores: Diagnostic LLM Evaluation via Fine-Grained Abilities
College of Computer Science；Technology, National University of Defense Technology, Changsha, Hunan, China；State Key Laboratory of Complex \& Critical Software Environment, Changsha, Hunan, China；National Key Laboratory of Parallel；School of Humanities；Social Sciences, School of Public Administration, Beihang University, Beijing, China
The Catastrophic Paradox of Human Cognitive Frameworks in Large Language Model Evaluation: A Comprehensive Empirical Analysis of the CHC-LLM Incompatibility
Structured Cognitive Loop for Behavioral Intelligence in Large Language Model Agents (Extended Revision: From Behavioral Architecture to Epistemic Accountability)
