人工智能时代中外合作办学基础生物化学课程知识图谱融入教学的构筑与实践

Construction and Practice of Integrating Knowledge Graphs into General Biochemistry Teaching under Sino-Foreign Cooperative Education in the AI Era

摘要: 针对人工智能(AI)时代中外合作办学《基础生物化学》课程全英文教学中面临的知识呈现碎片化、学情差异化等痛点，本工作探索了知识图谱与AI技术深度融合的教学模式。依托智慧教学平台，教学团队开展了多维层级知识图谱的协同构建，实现跨课程与跨章节知识点的有效连接；引入大语言模型辅助知识图谱构建与PBL情境教学设计；融合中外教师跨文化视域共铸高阶思想育人节点；结合中外数字化工具(超星学习通与EchoPoll)实施数据驱动的全过程互动教学闭环，并在教学过程中规范AI使用。问卷调查数据表明，学生对该授课模式的满意度达100%；96.43%的学生认为中外数字化工具互补显著提升了课堂互动与沉浸感；96.43%的学生认可知识图谱对理解课程内容的帮助。本次教学实践表明，“图谱驱动、AI赋能、中外协同”的教学模式有效破解了传统全英文教学瓶颈，显著降低了跨语言认知负荷，为人工智能时代中外合作办学背景下课程全英文教学提供了一个有益的探索和参考案例。

Abstract: To address the challenges of knowledge fragmentation and diverse learner profiles in fully English-taught courses under Sino-foreign cooperative education, this study explores an innovative pedagogical model integrating knowledge graphs and artificial intelligence (AI) for a General Biochemistry course. Supported by intelligent educational platforms, a multi-dimensional hierarchical knowledge graph was constructed to facilitate cross-disciplinary and inter-chapter knowledge connections. Large language models (LLMs) were employed to assist in the construction of the knowledge graph and the design of Problem-Based Learning (PBL) instructional contexts. Furthermore, a cross-cultural collaborative approach was adopted by Chinese and Australian instructors to co-construct high-order, value-shaping educational nodes. By combining complementary digital tools (Chaoxing Xuexitong and EchoPoll), a data-driven, closed-loop interactive teaching environment was implemented, alongside the establishment of clear ethical guidelines for AI usage. Empirical results from student surveys indicate a 100% overall satisfaction rate with the proposed teaching model. Specifically, 96.43% of the participants reported that the digital tools significantly enhanced the class participation, and an equal percentage (96.43%) acknowledged the knowledge graph’s effectiveness in facilitating content comprehension. These findings suggest that the “Graph-Driven, AI-Empowered, and Cross-Culturally Collaborative” teaching framework effectively overcomes the limitations of traditional English-taught courses and significantly mitigates cross-linguistic cognitive load. This study provides a highly valuable practical paradigm for fully English-taught courses in Sino-foreign cooperative education in the AI era.