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Updated: Feb 28, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Knowledge graph-based cognitive learning with multi-fact reasoning.

Chengfeng Liu1, Jianrui Chen2, Zhihui Wang1

  • 1School of Artificial Inteligence and Computer Science, Shaanxi Normal University, Xi'an, 710119, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new Cognitive Diagnosis framework (CD-SKG) to improve personalized learning by analyzing student-exercise-concept interactions. The model captures complex, higher-order relationships for more accurate cognitive state diagnosis and performance prediction.

Keywords:
Cognitive diagnosisHigher-order informationHypergraph convolutional networkKnowledge graphMulti-fact reasoning

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Area of Science:

  • Artificial Intelligence in Education
  • Educational Data Mining
  • Cognitive Science

Background:

  • Intelligent education systems personalize learning by analyzing student, task, and concept interactions.
  • Cognitive diagnosis (CD) models predict student performance but struggle with complex student-exercise-concept relationships and higher-order interactions.

Purpose of the Study:

  • To propose a novel Cognitive Diagnosis framework based on Signed Knowledge Graph with multi-fact reasoning (CD-SKG).
  • To address limitations in existing CD models regarding complex and higher-order interactions among students, exercises, and concepts.

Main Methods:

  • Modeled students, exercises, and concepts as signed facts to encode response valence and capture tripartite interactions.
  • Employed a dual-view hypergraph convolutional network on a signed cognitive hypergraph to learn response features.
  • Analyzed higher-order relations at two diagnosis levels by integrating tripartite cognitive factors.

Main Results:

  • The CD-SKG framework achieved optimal performance on four real-world datasets.
  • Outperformed eight state-of-the-art Cognitive Diagnosis models.
  • Demonstrated effective capture of complex and higher-order interactions.

Conclusions:

  • The proposed CD-SKG framework significantly enhances cognitive diagnosis accuracy in intelligent education systems.
  • It provides a robust method for modeling intricate relationships within educational data.
  • The findings pave the way for more sophisticated personalized learning experiences.