With the rapid development of intelligent educational technologies, traditional higher-level mathematics instruction is plagued by widespread problems such as inefficient resource utilization, insufficient personalized instruction, and low student learning initiative. To address these shortcomings, this paper introduces artificial intelligence and big data analysis technologies to construct an AI-driven online and offline hybrid teaching model for higher-level mathematics. Intelligent learning analysis enables accurate identification of student knowledge mastery. A dynamic question bank and visual auxiliary resources enable real-time adaptation of learning resources. The "Cloud Classroom" platform provides intelligent feedback and dynamic adjustment of classroom interactions, comprehensively enhancing detailed control over the teaching process. An empirical study, conducted on undergraduate engineering students, designs a controlled experiment. Results show that the experimental group, with a mean score of 78.2, outperforms the control group (68.5) on the interim test, with a significant difference (p=0.003), demonstrating the sustained superiority of the AI hybrid teaching model in interim learning outcomes. Finally, the pass rate indicator shows a statistically significant 98.0% for the experimental group and 86.0% for the control group (p=0.014), further validating the effectiveness of this teaching model in reducing the risk of failure. The research results verify the significant advantages of blended teaching empowered by artificial intelligence in improving the accuracy and effectiveness of higher mathematics teaching.

Suxiang Zhang. Practice of Hybrid Teaching of Advanced Mathematics Based on Artificial Intelligence. Frontiers in Educational Research (2025), Vol. 8, Issue 8: 113-120. https://doi.org/10.25236/FER.2025.080818.

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