Mechanisms and Pathways for Empowering University Career Guidance through AI Agents in a Human-Machine Collaboration Context: An Empirical Study Based on TPC Theory

Abstract

With the rapid advancement of generative artificial intelligence, the use of AI agents to address the long-standing mismatch between supply and demand in university career guidance has become increasingly feasible. This study examines four types of AI-based career guidance agents—resume optimization, interview training, career planning, and psychological counseling—implemented in the School of Accounting at a Chinese university. Grounded in Task–Performance Chain (TPC) theory, which integrates the core logic of task–technology fit, this research adopts a mixed-methods approach combining questionnaire surveys and in-depth interviews. A total of 207 graduating students were surveyed to systematically analyze their user experiences and feedback. The findings reveal that the empowering effects of AI agents in career guidance are not evenly distributed but instead exhibit a pronounced pattern of “asymmetric empowerment.” Specifically, in highly standardized tasks such as resume optimization, AI agents function through an “efficiency substitution mechanism” based on rule-driven automation. In semi-structured tasks such as interview training, they operate through a “capability enhancement mechanism” enabled by scenario-based simulations. In contrast, in low-structured tasks such as psychological counseling, a “trust inhibition mechanism” emerges due to limitations in emotional engagement. Consistent with prior research, the distinction between “instrumental trust” and “relational trust” appears to be a critical factor influencing students’ willingness to adopt AI agents, a conclusion further supported by the empirical data of this study. Based on these findings, this paper proposes the construction of a “tiered and differentiated” human–machine collaborative ecosystem. It also redefines the role of career counselors as both “human–machine interface coordinators” and “gatekeepers of emotional value.” These insights provide both theoretical contributions and practical implications for the digital transformation of university career guidance. Keywords generative artificial intelligence; educational agents; career guidance; task–performance chain; human–machine collaboration; digital competence 102-111 Mechanisms and Pathways for Empowering University Career Guidance through AI Agents in a Human-Machine Collaboration Context An Empirical Study Based on TPC Theory 下载 Pages: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Share this: Share on X (Opens in new window) X Share on Facebook (Opens in new window) Facebook Like Loading…