The Role of Artificial Intelligence Anxiety and Attitudes Toward Artificial Intelligence in University Students' Job Finding Anxiety

Synthesis: AI anxiety, attitudes, and job-finding anxiety — a large correlational survey of 1,057 university students across 35 Turkish universities showing that higher AI anxiety and negative attitudes toward AI are positively associated with job-finding anxiety. Female students, social-science majors, and second-year students reported higher anxiety levels.

Üstün & Danacıoğlu (2026) scale up the career-anxiety evidence base, linking AI-related anxiety and attitudes directly to post-graduation job-finding anxiety. It complements the mechanism-focused studies (Duan et al., Wang et al.) with population-level correlational breadth and demographic patterning.

Method

• Sample: 1,057 students from various faculties and departments across 35 universities in Türkiye.
• Design: Correlational survey, online administration via convenience and snowball sampling.
• Analysis: Correlation, independent-samples t-test, ANOVA, Cohen's d.

Key Findings

• Higher AI anxiety → higher job-finding anxiety. AI-related anxiety is positively associated with post-graduation job-finding anxiety.
• Negative AI attitudes → higher job-finding anxiety. Students with more negative attitudes toward AI report greater employment concern.
• Demographic patterning: female students, social-science majors, and second-year students reported higher anxiety levels — pointing to equity dimensions of Anxiety and Stress and Career Development and Readiness.

What this means for practice

• Instructors. Bring AI and labor-market change into coursework rather than leaving it to career offices: with 1,057 students, higher AI anxiety and more negative AI attitudes were associated with greater job-finding anxiety, so discuss how AI is reshaping graduate employment in the disciplines you teach.
• Instructors. Do not treat anxiety as uniform across a cohort — female students, social-science majors, and second-year students reported the highest levels, so check which groups in your class are carrying the most concern.
• Administrators. Target support where the data point: prioritize career counseling, psychosocial assistance, and skills-based training for female and social-science students rather than allocating services evenly.
• Administrators. Schedule career-orientation, digital-adaptation, and labor-market preparation initiatives early, since second-year students reported higher anxiety than the overall sample pattern would suggest.
• Administrators. Expand internships, project-based learning, and structured mentoring for social-science students, for whom AI-supported employability tools can open routes that technical training alone does not.

Limitations

• Correlational and cross-sectional: the participants' views were collected through three scales with no experimental procedures or interventions, and the authors state the design cannot reveal causal relationships between job-finding anxiety, AI anxiety, and AI attitudes.
• Convenience plus snowball sampling through online forms between February 15 and April 15, 2025, beginning with 232 students at the researchers' own university — participation was voluntary, so the 1,057 respondents self-selected.
• All three constructs were measured by self-report scales, leaving shared method variance and social desirability as possible explanations for part of the association.
• The 35-university sample improves on the single-site study the authors cite as their motivation, but it remains a non-probability sample drawn from one national context, and the authors recommend structural equation modeling or mixed methods before causal claims.

Citation

Üstün, A. G., & Danacıoğlu, N. (2026). The Role of Artificial Intelligence Anxiety and Attitudes Toward Artificial Intelligence in University Students' Job Finding Anxiety. Participatory Educational Research, 13(2), 143–164.