Synergizing Knowledge Graphs and LLMs
An Intelligent Tutoring Model for Self-Directed Learning
Bibliographic Data
| ID | 22044009 |
|---|---|
| Authors | Guixia Wang (0000-0001-8107-616X, South China Normal University), Zehui Zhan (0000-0002-6936-1977, South China Normal University, corresponding author), Shouyuan Qin (Hubei Normal University) |
| Year | 2025 |
| Volume | 15 |
| Issue | 9 |
| Pages | 1102 |
| Publication date | 2025-08-25 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci15091102 |
| OpenAlex | W4413669405 |
| Language | EN |
| Citations received | 1 |
| References cited | 18 |
General large language models (LLMs) often suffer from semantic misinterpretation, information redundancy, and hallucinated content when applied to educational question-answering tasks. These issues hinder their effectiveness in supporting students’ specialized course learning and self-directed study. To address these challenges, this study proposes an intelligent tutoring model that integrates a knowledge graph with a large language model (KG-CQ). Focusing on the Data Structures (C Language) course, the model constructs a course-specific knowledge graph stored in a Neo4j graph database. It incorporates modules for knowledge retrieval, domain-specific question answering, and knowledge extraction, forming a closed-loop system designed to enhance semantic comprehension and domain adaptability. A total of 30 students majoring in Educational Technology at H University were randomly assigned to either an experimental group or a control group, with 15 students in each. The experimental group utilized the KG-CQ model during the answering process, while the control group relied on traditional learning methods. A total of 1515 data points were collected. Experimental results show that the KG-CQ model performs well in both answer accuracy and domain relevance, accompanied by high levels of student satisfaction. The model effectively promotes self-directed learning and provides a valuable reference for the development of knowledge-enhanced question-answering systems in educational settings
Cognitive psychology · Knowledge management · Mathematics education · Computer Science · Intelligent Tutoring Systems and Adaptive Learning · Psychology · Text Readability and Simplification · Topic Modeling · Artificial Intelligence
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Adapting to the Future
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |