Research on Clustering Analysis of Generative AI Tool Usage Behaviors Among College Students

This study investigates how university students use generative AI tools by applying the K-means clustering algorithm to analyse their usage frequency, scenarios and purposes. Based on virtual questionnaire data, students were effectively grouped into different behavioural categories. The results show clear differences between high and low frequency users in terms of time spent, motivations and learning outcomes - differences that are largely influenced by students' learning goals and the specific tools they use. A key innovation of this research is the use of clustering analysis to map the diversity of AI tool usage behaviours, providing valuable insights for personalised educational strategies. The findings also provide a solid data base for universities to refine the use of AI tools in learning, with the ultimate goal of improving student performance and guiding the future integration of AI in educational contexts.