AI赋能高等数学课程混合式教学改革研究与探索

Research and Exploration on the Reform of Blended Teaching in Higher Mathematics Courses Empowered by Artificial Intelligence

摘要: 在教育数字化转型加速推进的背景下，高等数学作为理工科人才培养的核心基础课程，长期面临知识抽象性强、学生认知差异显著、教学模式固化、评价体系单一等现实困境。人工智能(AI)技术与混合式教学的深度融合，为破解这些难题提供了创新路径。本文基于布鲁姆教学目标分类理论与建构主义学习理论，通过文献研究、问卷调查、案例分析等方法，系统梳理AI与高等数学教学融合的研究现状与局限，遵循“理论奠基–现状洞察–机制构建–实践思路”的研究逻辑，提出“智能资源供给–个性化教学实施–动态评价反馈”三位一体的混合式教学改革机制，并以线性代数等高等数学核心课程为载体，设计具体改革实践思路。研究旨在为高等数学课程数字化转型提供理论支撑与可探索的实践方向，助力提升教学精准度与学生学习主动性。

Abstract: Against the backdrop of accelerated digital transformation in education, higher mathematics, as a core foundational course for cultivating talents in science and engineering disciplines, has long faced practical challenges such as high abstraction of knowledge, significant differences in students’ cognitive abilities, rigid teaching models, and single evaluation systems. The in-depth integration of Artificial Intelligence (AI) technology and blended teaching provides an innovative path to address these issues. Based on Bloom’s Taxonomy of Educational Objectives and Constructivist Learning Theory, this study systematically sorts out the research status and limitations of the integration of AI and higher mathematics teaching through methods such as literature research, questionnaire surveys, and case analysis. Following the research logic of “theoretical foundation-status insight-mechanism construction-practical ideas”, it proposes a “trinity” blended teaching reform mechanism consisting of “intelligent resource supply-personalized teaching implementation-dynamic evaluation and feedback”. Taking core higher mathematics courses such as linear algebra as carriers, specific reform and practice ideas are designed. This research aims to provide theoretical support and explorable practical directions for the digital transformation of higher mathematics courses, and help improve teaching accuracy and students’ learning initiative.