@inproceedings{yu-etal-2026-cognet,
    title = "{C}og{N}et-{KG}: Empowering Tutoring Dialogues with a Cognitively-Structured Knowledge Graph for {STEM} Learning",
    author = "Yu, Ding  and
      Lu, Yu  and
      Li, Tengju  and
      Xiong, Shasha  and
      Yu, Shengquan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.639/",
    pages = "13101--13121",
    ISBN = "979-8-89176-395-1",
    abstract = "Educational knowledge graph (EKG) is a critical component of intelligent tutoring systems that is structured around cognitive principles and provides support for interactive teaching. Most existing EKGs usually rely on simplistic relations, bind with single subjects, and lack integration with explicit learning objectives. In this paper, we introduce CogNet-KG, a novel and cognitively-structured large-scale knowledge graph for STEM learning. CogNet-KG models nearly 500 core concepts across five subjects with various cognitively-grounded relations corresponding to specific learning objectives, thereby encoding a rich cognitive schema for guiding more effective teaching. Based on this structure, we then construct a high-quality tutoring dialogue dataset CogDialogue-QA by leveraging adaptive instructional strategies. Additionally, we train CogTutor-LM, a specialized tutorial LLM that internalizes this structured pedagogical reasoning. Overall evaluation demonstrates that CogTutor-LM generates responses with significantly greater instructional coherence and more appropriate pedagogical guidance compared to baselines, validating the effectiveness of our graph-driven approach to fostering knowledge integration and stimulating students' thinking. The datasets are publicly available at https://github.com/KCAIED/CogNet-KG."
}