Core features and feature relationships of male and female management students' attitudes towards GenAI: A network analysis of a Guangxi university sample.

Students' attitudes towards generative artificial intelligence (GenAI) may shape how they use these tools in learning and how they understand their relevance for future work. However, relatively little is known about whether male and female management students differ in the structure of their GenAI attitudes. Using network analysis, the present study examined the core features and feature relationships of GenAI attitudes in a sample of 887 management students from one university in Guangxi, China (45.1% male, 54.9% female). In the male network, the core features were "AI controlling people risk" (GAAIS 18) and "AI promotes happiness" (GAAIS 9), whereas in the female network, the core features were "AI in my management learning" (GAAIS 5) and "AI controlling people risk" (GAAIS 18). Network comparison showed no significant between-group differences in overall network structure (p = .527) or global strength (p = .840), suggesting broad similarity at the global level. However, several local associations differed between the two networks, indicating that male and female students did not organise specific positive and negative attitude features in exactly the same way. These findings provide a more fine-grained understanding of gender-related patterns in GenAI attitudes among management students and may inform future research and educational support in higher education.