Abstract

Information technology develops fast. Generative AI is now used in educational scenarios, and it can help transform traditional teaching models into intelligent systems. Traditional education has long faced practical challenges such as heavy workload for teachers in lesson preparation, lagging diagnosis of student situations, great difficulty in visualizing abstract knowledge, and difficulties in implementing differentiated teaching. However, generative AI, with its technical capabilities in text generation, image animation creation, and big data-based student situation analysis, provides a new solution to address the bottlenecks in educational development. Now, generative AI has shown clear benefits in improving teaching and learning efficiency. But in practical use, some problems still exist. The tool use is shallow, and the protection system for data security of teachers and students is inadequate. In this study, we systematically explore the three core auxiliary functions of generative AI in university teaching, namely dynamic monitoring of students’ learning status, automatic generation of teaching courseware, and intelligent creation of subject-specific teaching videos and animations. It also demonstrates the teaching empowerment value of AI large models by addressing the practical difficulties of traditional teaching. Based on this, it objectively examines the advantages and shortcomings of current AI education applications, summarizes the practical logic of human-machine collaborative teaching, and makes development prospects for the long-term transformation and upgrading of traditional education driven by generative AI from three dimensions: deep integration of courses, cultivation of teachers’ digital literacy, and establishment of a smart teaching system. The aim is to provide theoretical references and practical ideas for the construction of smart education in universities and the digital reform of education.