The Boundaries and Regulatory Mechanisms of Generative AI-Assisted Teaching Applications in Higher Education in China

Abstract

Generative AI empowers the digital transformation of higher education. The issues surrounding its application boundaries and regulatory mechanisms have garnered widespread attention. However, existing research often focuses on a single risk, lacking a systematic exploration. Moreover, relevant regulations and industry standards lag behind technological development, hindering the deep integration of technology and education. This paper explores the application boundaries and regulatory mechanisms of generative AI-assisted teaching in Chinese higher education. The analysis reveals that current generative AI in university teaching faces issues such as insufficient technical adaptability, academic ethics violations, and data security risks. Clarifying application boundaries and establishing a scientific regulatory system are crucial for regulating technology application and promoting high-quality education development. Based on this, this paper proposes suggestions for establishing AI application standards and boundaries tailored to teaching needs, improving a collaborative regulatory system involving multiple stakeholders, and developing a full-chain governance approach. It is of great significance to standardize the application of technology and promote the high-quality development of education.

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