The quality of higher education has attracted global attention, and foundational courses shoulder the critical mission of core knowledge imparting. Therefore, improving the quality of higher education must start with enhancing the teaching quality of foundational courses. Currently, the teaching of foundational disciplines faces dilemmas including a single teaching method, excessively large class sizes, and a lack of hierarchical educational objectives. These issues hinder students’ learning initiative and effective interaction between teachers and students, resulting in insufficient momentum for improving teaching quality. To address the aforementioned problems, this paper introduces AI knowledge graph technology and proposes a hierarchical teaching model, taking Advanced Mathematics as an example, to achieve precise learning situation diagnosis, dynamic stratification, and intelligent resource matching. A controlled experiment was carried out with 100 undergraduates from two parallel engineering classes as research subjects. The experimental group adopted the AI knowledge graph-driven hierarchical teaching model, while the control group used the traditional teaching model. Results indicate that the experimental group achieved a final average score of 81.96 (compared to 76.72 in the control group, p=0.018**), a pass rate as high as over 94%, and was significantly superior to the control group in self-directed learning time (8 hours/week vs. 7 hours/week in the control group).The research confirms that this model can effectively enhance teaching precision and learning effectiveness, offering practical references for the intelligent reform of foundational discipline teaching in universities.

Yao Zhang, Xinghua Hu. Practice of Hierarchical Teaching for Advanced Mathematics Based on AI Knowledge Graph. Frontiers in Educational Research (2026), Vol. 9, Issue 1: 17-24. https://doi.org/10.25236/FER.2026.090103.

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