Research on Digital Transformation Strategies for University Curricula Integrating AI Technologies
Year: 2025
Type: conference-paper
Study design: Clinical Trial
Abstract: This study focuses on the practical application and effect verification of intelligent technology in the digital transformation of university teaching. With the rapid development of information technology, traditional university courses have gradually shown certain shortcomings in content presentation and teaching interaction. Existing studies use more algorithms such as machine learning to improve classroom efficiency and students' learning experience. To this end, we proposed a set of digital transformation strategies for university courses and verified them through empirical research. The study selected two university courses, “Advanced Mathematics” and “Crawler Technology”, and adopted the integration model of “technology-assisted + traditional teaching” to carry out teaching reform. The purpose is to explore the specific situation of new tools in enriching course content, expanding knowledge coverage, improving learning outcomes, improving student satisfaction, and stimulating online learning activity. We also introduced relevant technologies such as natural language processing and data mining to construct a quantitative evaluation index system. By setting up an experimental group and a control group, a comparative analysis was conducted through data such as students' academic performance, platform usage records, and course feedback. The results show that the course content has become more layered after integrating new technologies, and students' learning performance has improved significantly, with satisfaction increasing from 70% to 91%, academic performance improving by an average of about 15%, and online learning participation increasing by 20%. These data verify the positive role of intelligent means in course optimization from multiple dimensions.
Authors: Shihua Dong, Simeng Fu
Institution: Guangdong University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412789956