Research on the Practical Path of Generative AI Empowering Teaching Reform of Probability and Mathematical Statistics in Chinese Tertiary Education

Abstract

Probability and Mathematical Statistics is a fundamental public mathematics course widely offered to science, engineering, and economics–management students in Chinese universities. ^[1] Driven by global educational digitalization and national initiatives of emerging engineering and emerging liberal arts, traditional teaching has been constrained by overemphasis on abstract theories, insufficient practical instruction, limited personalized support, and oversimplified assessment systems. These shortcomings severely hinder the cultivation of data literacy and statistical reasoning abilities required by contemporary talent development. As an advanced technological tool, generative artificial intelligence presents great potential for innovating instructional design and improving learning effectiveness in higher mathematics education. Based on authoritative data from the Ministry of Education of China, national teaching surveys, and institutional teaching practices, this study identifies core challenges in current probability and statistics education, analyzes the transformative value of generative AI for teaching improvement, and proposes a systematic reform framework covering curriculum optimization, pedagogical innovation, practical training enhancement, and diversified assessment construction. This paper also clarifies ethical boundaries and practical principles for the responsible integration of generative AI. The conclusions and pathways are intended to provide reliable, evidence-based references for advancing the high-quality development of probability and statistics teaching in Chinese tertiary education.