问题图谱对大语言模型支持下的自主式学习影响机制分析

Analysis of the Mechanism of Influence of Problem Graphs on Autonomous Learning Supported by Large Language Models

摘要: 针对人工智能发展影响下的学习新生态问题，本文分析了大语言模型在自主式学习中的优势与局限性，并以此为基础系统探讨了问题图谱与大语言模型在自主式学习中的协同逻辑，进而深入分析了问题图谱对大语言模型支持下的自主式学习的影响机制。研究发现，问题图谱通过结构化知识、优化学习路径和提供认知支持三大核心机制，能够有效克服大语言模型在自主学习中的“幻觉”问题，达到提高学习效率的效果。本文揭示了问题图谱与大语言模型协同工作时的动态交互模式，为构建更高效、可靠的智能自主学习系统提供了理论框架与实践指导。

Abstract: In response to the issue of the new learning ecosystem influenced by the development of artificial intelligence technology, this paper analyzes the advantages and limitations of large language models in autonomous learning, and on this basis, systematically explores the collaborative logic of problem maps and large language models in autonomous learning. Furthermore, it deeply analyzes the influence mechanism of problem maps on autonomous learning supported by large language models. The research finds that problem maps can effectively overcome the “hallucination” problem of large language models in autonomous learning through three core mechanisms: structuring knowledge, optimizing learning paths, and providing cognitive support, thereby achieving the effect of improving learning efficiency. This paper reveals the dynamic interaction mode when problem maps and large language models work together, providing a theoretical framework and practical guidance for building more efficient and reliable intelligent autonomous learning systems.