The impact of career adapt-abilities on AI anxiety among English majors: a dual perspective analysis based on core self-evaluations at the person- and variable-centered

Synthesis: Career adapt-abilities and AI anxiety — a study of 444 English-major undergraduates at four Chinese universities (July–August 2025) showing that career adapt-abilities significantly and negatively predict AI anxiety, with core self-evaluations partially mediating this relationship. Latent profile analysis identified three career adapt-abilities subgroups (low, medium, high): the low group showed the highest AI anxiety, the high group the lowest. This is direct empirical evidence that career-related readiness is a protective factor against AI anxiety.

Wang (2026) connects the knowledge base's Anxiety and Stress and career-readiness threads: students who feel more adaptable and prepared for their careers experience less AI anxiety. It positions career development as a buffer against the fear that AI will displace or devalue one's professional future.

Method

• Sample: 444 English-major students from four comprehensive universities in Sichuan, China.
• Design: Dual-perspective — variable-centered (regression/mediation) and person-centered (latent profile analysis).
• Measures: Career Adapt-Abilities Scale, Core Self-Evaluation Scale, and an AI Anxiety Scale.

Key Findings

• Career adapt-abilities negatively predict AI anxiety. Students with higher career adaptability report significantly lower AI anxiety.
• Core self-evaluations partially mediate the relationship — career adaptability works partly by strengthening positive core self-evaluations (self-worth, competence), which in turn reduce anxiety.
• Three latent profiles (low, medium, high career adapt-abilities) with significant differences in core self-evaluations and AI anxiety; the low group had the highest AI anxiety and the high group the lowest.
• Both analytic strategies converge, strengthening the robustness of the finding.

What this means for practice

• Instructors. Teach AI literacy as psychological adaptation, not tool exposure: AI usage habits showed no significant link to AI anxiety in this sample, so pair tool instruction with discussion of AI's limits and case studies of language professionals collaborating with AI.
• Instructors. Treat career adapt-abilities as a cultivable resource and build them into coursework through skill demonstration, positive feedback, and accumulated success experiences, since they negatively predicted AI anxiety and the lowest of the three latent profiles had the highest anxiety.
• Instructors. Reinforce core self-evaluations deliberately — the mediation analysis shows career readiness lowers anxiety partly by strengthening students' sense of professional worth and competence.
• Administrators. Require AI-aware portfolios (for example bilingual content strategy, localization, terminology management, and AI-assisted editing logs) and strengthen employer partnerships so that training translates into labor-market advantage for English and humanities graduates.
• Administrators. Offer evidence-based counseling such as cognitive-behavioral therapy for students showing low self-efficacy, high neuroticism, or an external locus of control, the profile most exposed to AI anxiety.

Limitations

• Cross-sectional design: the authors state it precludes causal inference about the ordering of career adapt-abilities, core self-evaluations, and AI anxiety, and note the reverse sequence is plausible — high anxiety reducing engagement in career planning.
• Effect sizes are modest: the mediation model explained only 14.1% of the variance in AI anxiety, and the indirect path through core self-evaluations accounted for 24% of the total effect.
• All three constructs were self-reported, risking social desirability bias and shared method variance; Harman's single-factor test was the only check, and the authors recommend marker-variable or multi-trait multi-method designs.
• 444 English majors from four universities in Sichuan: the authors note eastern coastal regions with more developed language-services industries and more visible AI adoption may yield different profiles, and the study did not examine the antecedents of career adapt-abilities.

Citation

Wang, X. (2026). The impact of career adapt-abilities on AI anxiety among English majors: a dual perspective analysis based on core self-evaluations at the person- and variable-centered. Frontiers in Psychology, 17, 1767791.