Exploration of University Mathematics Teaching Reform in the Era of Artificial Intelligence

Abstract

The rapid advancement of artificial intelligence (AI) presents transformative opportunities for higher education, particularly in mathematics. However, current university mathematics education faces critical challenges in adapting to the AI era. Traditional curricula overemphasize manual computation and theoretical proofs, neglecting the integration of computational tools and real-world applications, which are
essential in the data-driven age. While AI technologies like GPT-4 and Chinese models (e.g., Wenxin Yiyan, DeepSeek) show potential, their
educational adoption remains limited due to accessibility barriers, cost, and insufficient contextualization for pedagogical needs. Additionally, teaching methods struggle to address personalized learning demands, relying on uniform approaches that fail to leverage AIs capabilities
in adaptive feedback and resource generation. Outdated assessment systems also inadequately evaluate students problem-solving skills and
mathematical modeling proficiency. Faculty digital literacy gaps further hinder innovation, with many educators lacking training to ethically
and effectively implement AI tools. To address these issues, reforms should prioritize modernizing curricula with AI-relevant content, fostering interactive and application-oriented pedagogies, and enhancing teacher competencies in data-driven education. By aligning mathematical
training with AI advancements, universities can cultivate critical thinking and technical agility, preparing students for future interdisciplinary
challenges.