AI学习工具在高等数学教学中的应用效果与效率提升路径研究

随着人工智能技术的不断发展，AI学习工具在高等教育领域的应用日趋广泛。高等数学作为大学理工科学生的核心基础课程，其学习成效和解题效率直接影响学生的逻辑思维与专业素养。本文以大学生高等数学学习为研究对象，基于人工智能赋能的教育技术视角，分析了传统教学中学生解题效率低下的主要成因，包括学习动机不足、教师反馈滞后及教学资源碎片化等问题。在文献综述与案例分析的基础上，探讨了AI学习工具通过知识图谱构建、实时智能诊断与个性化练习推送等机制实现效率提升的内在逻辑。研究结果表明，AI学习工具能有效促进学生从“机械套用公式”向“逻辑化与系统化思维”转变，显著改善问题解决效率与思维质量。同时，文章指出了AI应用中存在的技术可靠性、学生依赖性及教学设计精准度不足等挑战，并提出了以“AI赋能—教师引导—学生反思”为核心的协同改进路径。研究旨在为高等数学课程的智能化教学提供理论参考与实践启示。With the continuous development of artificial intelligence technology, AI learning tools have been increasingly widely applied in the field of higher education. As a core foundational course for university students majoring in science and engineering, higher mathematics exerts a direct impact on students' logical thinking and professional literacy through its learning effectiveness and problem-solving efficiency. Focusing on college students' higher mathematics learning, this paper, from the perspective of educational technology empowered by artificial intelligence, analyzes the main causes of students' low problem-solving efficiency in traditional teaching, including insufficient learning motivation, delayed teacher feedback, and fragmented teaching resources. Based on a literature review and case analysis, the paper explores the internal logic through which AI learning tools enhance efficiency via mechanisms such as knowledge graph construction, real-time intelligent diagnosis, and personalized exercise recommendation. The research results show that AI learning tools can effectively promote the transformation of students' thinking from "mechanical application of formulas" to "logical and systematic thinking," significantly improving problem-solving efficiency and the quality of thinking. Meanwhile, the paper points out challenges existing in the application of AI, such as insufficient technical reliability, excessive student dependence, and inadequate accuracy in instructional design. It further proposes a collaborative improvement path centered on the "AI empowerment - teacher guidance - student reflection" model. The study aims to provide theoretical references and practical insights for the intelligent teaching of higher mathematics courses.