Narrative Generation and Ideological and Political Guidance Strategies for College Students' Employment Anxiety under the Impact of Artificial Intelligence

Abstract

The fear of losing their jobs has grown more salient for college students in the face of the speedy development of generative artificial intelligence and its implications for the nature of work. Little existing research has explored anxiety emotions in relation to narrative and how they are constructed and spread on this level, with the majority of studies being on the psychological variables or on larger employment outcomes. This research uses social constructivist narrative theory and LABOV narrative six element analysis method, with 7 fresh graduates of various academic disciplines as subjects, to find out the process of employment anxiety narratives generated by the subjects. The results show that the central theme of “AI's impact on employment” passes through two pivotal behavioral nodes, namely “whether to engage in AI-related education” and “how to explain failure,” resulting in three role stories: “victims,“ “observers,“ and “resisters. Metaphoric devices and final admonitions/lectures are used to achieve social emotional diffusion in these narratives. Based on these findings, the study recommends a political education strategy focused on "narrative reconstruction" that includes changing narrative motifs, helping to differentiate their roles, improving discursive symbols and building an integrated "curriculum-service-practice" political education system. It is systematic negative anxiety narrative deconstruction, which helps students change the negative narrative of technology into a positive one, and helps to provide solutions to ideological and political education that can actively adapt to the AI era.