The framework generalizes under automatically generated Q-matrices without requiring expert annotation¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? Consistent improvements across three Q-matrix construction methods (community-provided, programmatic keyword/LLM-tag extraction, attribute grouping) confirm generalization without expert annotation. → Yagna Manasa Boyapati 2026

Evidence¶

Yagna Manasa Boyapati 2026¶

Yagna Manasa Boyapati, Chong Yu, Tianyu Jiang, Justin Zhan. (2026). Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis. arXiv preprint. https://arxiv.org/abs/2609.02947 doi:10.48550/arxiv.2609.02947

q2 · i? · design · r2

Q-matrix automatic extraction analysis in the experimental setup: ASSIST09 uses a community-provided Q-matrix, GSM8K uses keyword matching plus GPT-4o-mini-generated concept tags, and UCI derives concepts via attribute grouping and normalization.

"Consistent improvements across all three construction methods confirm that our framework generalizes well under automatically generated Q-matrices, without requiring expert annotation."

Discussion¶

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