Construction and Application of a Model for Enhancing Intrinsic Motivation for Employment Among Gen Z Foreign Language Majors Engaged “Slow Employment” in the AI Era

Abstract

With the number of university graduates continuing to rise and the explosive development of generative artificial intelligence (AI), traditional language-related industries are undergoing a disruptive transformation. The convergence of fears regarding technological unemployment and the cognitive bias of ‘the irrelevance of one’s major’ has led to an increasingly prominent phenomenon of ‘slow employment’ among English language graduates. Enhancing the intrinsic motivation for employment within this group is a key task for universities in supporting graduate employment. Grounded in positive psychology theory, this study systematically analyses the current status and influencing factors of the intrinsic motivation for employment among Generation Z English majors experiencing “slow employment”. Drawing upon the Positive Emotions, Engagement, Relationships, Meaning, and Accomplishment (PERMA) theory, it constructs a systematic model for enhancing this motivation and clarifies its practical application across three dimensions: psychological assessment for employment, personalised career guidance, and collaborative education involving families, schools and the community. This research provides theoretical support and practical pathways for universities to address the “slow employment” dilemma faced by foreign language graduates and to optimise employment education in the era of intelligent technology. Keywords Positive psychology, Generation Z English majors, Slow employment, Intrinsic motivation for employment, PERMA model 149-163 Construction and Application of a Model for Enhancing Intrinsic Motivation for Employment Among Gen Z Foreign Language Majors Engaged Slow Employment in the AI Era 下载 Pages: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Share this: Share on X (Opens in new window) X Share on Facebook (Opens in new window) Facebook Like Loading…