PICKT self-reported: difficulty features most informative for very hard items; text and knowledge-graph features estimate unseen items
Wonbeen Lee and three co-authors self-report that PICKT integrates multiple feature types, with experiments showing difficulty features are most informative for very hard items with extremely low accuracy, and text plus knowledge-graph features can estimate representations of unseen items.
ImportanceLocalEvidenceE2 unreplicated
New items in intelligent tutoring systems lack response histories, degrading diagnostic reliability; after PICKT integrates multiple feature types, experiments show difficulty features are most informative for very hard items with extremely low accuracy, and fusing text and knowledge-graph features can estimate representations of unseen items via semantically or structurally similar items seen in training; the authors suggest prioritizing feature annotation according to educational-service needs. Results are self-reported by Wonbeen Lee and three co-authors.
Boundary: results are author self-reported with no third-party replication; the work first appeared in December 2025 and was updated as v2 on September 15, 2026 (arXiv:2512.07179).
Source: arXiv:2512.07179 abstract page ↗