Papers by Shengquan Yu
CogNet-KG: Empowering Tutoring Dialogues with a Cognitively-Structured Knowledge Graph for STEM Learning (2026.findings-acl)
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| Challenge: | Educational knowledge graphs are a critical component of intelligent tutoring systems that are structured around cognitive principles and provide support for interactive teaching. |
| Approach: | They propose a cognitively-structured large-scale knowledge graph for STEM learning that models nearly 500 core concepts across five subjects with various cognitively grounded relations corresponding to specific learning objectives. |
| Outcome: | The proposed model generates a high-quality tutoring dialogue dataset CogDialogue-QA and a specialized tutorial LLM that internalizes this structured pedagogical reasoning. |
Improving Prompt Generalization for Cross-prompt Essay Trait Scoring from the Scoring-invariance Perspective (2025.findings-emnlp)
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| Challenge: | Existing research on cross-prompt trait essay scoring focuses on improving model generalization by obtaining prompt-invariant representations. |
| Approach: | They propose a scoring-invariant learning objective that encourages the model to focus on intrinsic information within the essay that reflects its quality during training, thereby learning generic scoring features. |
| Outcome: | The proposed scoring-invariant learning objective encourages the model to focus on intrinsic information within the essay that reflects its quality during training, thereby learning generic scoring features. |