Author(s)

Yu Liu ¹

Affiliation(s)

¹ Golden Gate University, San Francisco, CA 94105, USA

Corresponding Author

Yu Liu

ABSTRACT

Artificial intelligence (AI) is transforming contemporary workplaces and generating new forms of career uncertainty. AI-related career anxiety has emerged as a psychological concern as individuals face changing work demands. Existing studies have primarily explained AI-related career anxiety as a direct response to technological disruption, with limited attention paid to the psychological process underlying individuals' experiences. We argue that understanding AI-related career anxiety requires shifting the focus from technological disruption itself to the psychological processes through which individuals interpret and adapt to AI-driven career change. Building on this perspective, we propose an integrated counseling psychology framework that synthesizes Cognitive Behavioral Theory (CBT), psychodynamic theory, humanistic-existential theory, and systems theory. The framework conceptualizes the development and maintenance of AI-related career anxiety as an evolving process in which cognitive appraisal, emotional responses, identity reconstruction, and meaning-making continuously interact within broader systemic contexts to shape individuals’ responses to AI-related career uncertainty. This perspective extends the existing technology-centered explanations and offers a more comprehensive understanding of how AI-related career anxiety develops and persists in the context of AI-driven workplace transformation. The proposed framework also provides a foundation for future empirical research and informs counseling practices aimed at fostering adaptive career development and psychological resilience in the AI era.

KEYWORDS

Artificial Intelligence; AI-Related Career Anxiety; Counseling Psychology; Psychological Mechanisms; Conceptual Framework

CITE THIS PAPER

Yu Liu. Conceptualizing AI-Related Career Anxiety: An Integrated Counseling Psychology Framework. Applied & Educational Psychology (2026). Vol. 7, No. 2, 16-23. DOI: http://dx.doi.org/10.23977/appep.2026.070203.

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