【摘要】 大语言模型在教育应用领域所呈现的知识冲突问题，表现为概念定义、事实陈述和逻辑推理等层面的认知不一致性，这种认知断裂严重制约了其在跨学科探究学习、深度认知任务和个性化教学等场景中的适用性和支持能力。该文系统分析了知识冲突的技术成因，包括训练数据中的噪声、参数化知识表示的局限、推理机制的缺陷、模型架构的先天不足以及外部知识的偏差，并探讨了这些因素对大语言模型教育应用的深层影响。针对这一挑战，论文提出了多维度的解决路径：通过数据增强优化知识表示，利用提示强化上下文的连贯，开发量规完善模型评估。同时，研究从社会文化的宏观视角进一步剖析了知识冲突的外部驱动因素，探讨如何在多元异质、动态演进的社会建构语境中，构建开放进取、兼容融通的智能教育应用体系。知识冲突的有效化解不仅可以显著提升大语言模型在教育场景中的应用价值，更将为人工智能在更广泛领域的可持续发展奠定坚实基础。研究旨在为解决这一问题提供理论洞见与实践指引，促进教育人工智能技术的可靠性、适应性和普及性的不断提升。更多还原

【Abstract】 Knowledge conflicts in large language models(LLMs) within educational applications manifest as cognitive inconsistencies across conceptual definitions, factual statements, and logical reasoning. These cognitive discontinuities significantly constrain their applicability and supportive capacity in interdisciplinary inquiry-based learning, deep cognitive tasks, and personalized instruction. This paper systematically analyzes the technical causes of knowledge conflicts, including training data noise, limitations of parameterized knowledge representation,reasoning mechanism deficiencies, inherent architectural constraints, and external knowledge biases, while exploring their profound implications for educational applications of LLMs. To address these challenges, the paper proposes multidimensional solutions: optimizing knowledge representation through data augmentation, enhancing contextual coherence via prompting strategies, and developing comprehensive metrics for model evaluation. Furthermore, the paper examines external drivers of knowledge conflicts from a macro socio-cultural perspective,investigating how to construct an open-minded and integrative intelligent education application system within a heterogeneous, dynamically evolving social constructivist context. Effective resolution of knowledge conflicts can not only significantly enhance the application value of LLMs in educational settings but also establish a solid foundation for the sustainable development of artificial intelligence across broader domains. This research aims to provide theoretical insights and practical guidance for addressing these issues, promoting continuous improvement in the reliability, adaptability, and accessibility of educational artificial intelligence technologies.更多还原