Work

CogNet-KG: Empowering Tutoring Dialogues with a Cognitively-Structured Knowledge Graph for STEM Learning

Year: 2026

Type: conference-paper

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 largescale knowledge graph for STEM learning.CogNet-KG models nearly 500 core concepts across five subjects with various cognitivelygrounded 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.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.639

Authors: Ding Yu, Yu Lu, Tengju Li, Shasha Xiong, Shengquan Yu

Institution: Beijing Normal University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Funder: National Natural Science Foundation of China