AI literacy alone is not enough: Student AI readiness and career adaptability in business and management education

Synthesis: Testa, Apuzzo, and Pittaway (2026) investigate how AI-related competencies contribute to career adaptability in business and management education. Surveying 339 university students in economics, management, and business programs in Italy, they employ a moderated mediation model examining the relationships among AI literacy, AI readiness, AI self-efficacy, and career adapt-abilities. Findings show AI readiness mediates the relationship between AI literacy and career adapt-abilities — with the indirect effect varying across levels of AI self-efficacy — and that AI self-efficacy positively moderates the literacy–readiness link while also directly associating with career adapt-abilities. The study positions AI readiness as a central mechanism linking AI-related learning to career-relevant outcomes, arguing that AI literacy alone does not directly translate into career adaptability.

Key Findings

AI readiness mediates the relationship between AI literacy and career adapt-abilities — literacy alone does not directly translate into career outcomes; competencies become career-relevant when translated into readiness to engage with AI in applied settings.

AI literacy predicts AI readiness (β = 0.148, p = .005), and AI self-efficacy also predicts AI readiness (β = 0.178, p = .005) — the model predicting AI readiness was significant (F(3,335) = 38.64, p < .001, R² = 0.257).

AI self-efficacy moderates the literacy–readiness link: the interaction was significant (β = 0.299, p < .001); the effect of AI literacy on AI readiness was not significant at low self-efficacy (β = −0.067) but positive at medium (β = 0.123) and high (β = 0.327) levels.

AI self-efficacy shows a direct positive association with career adapt-abilities (β = 0.255, p < .001) — a distinct motivational resource beyond literacy — and also interacts with AI literacy (β = 0.236, p < .001) in predicting career outcomes.

Moderated mediation confirmed: the indirect effect of AI literacy on career adapt-abilities through AI readiness was not significant at low self-efficacy (β = −0.012) but positive and significant at medium (β = 0.022) and high (β = 0.057) levels, with a significant index of moderated mediation (index = 0.052).

The full model predicting career adapt-abilities explained 40% of variance (F(4,334) = 55.75, p < .001, R² = 0.4004), with AI literacy (β = 0.120, p = .008) and AI readiness (β = 0.175, p < .001) as significant predictors.

Study Design & Method

This survey study collected data from 339 university students enrolled in economics, management, and business-related degree programs (including Computer and Data Science for Economics and Corporate Communication) at Italian higher education institutions, using an online questionnaire (Microsoft Forms) distributed between March and June 2025. Students had substantive exposure to AI within their curricula (group work, laboratories, or project-based assignments involving AI). Constructs were measured with validated scales: AI literacy, AI readiness (operationalized at the individual level as motivational and cognitive readiness), AI self-efficacy (Wang & Chuang, 2024), and career adaptability (Career Adapt-Abilities Scale – Short Form, measuring concern, control, curiosity, confidence). Items were translated from English with a back-translation procedure and pre-tested with 17 students. Hypotheses were tested using linear regression and moderated mediation analysis (PROCESS Model 8, version 4.0, Hayes 2022) in SPSS, with all variables standardized. Common-method bias was mitigated procedurally and statistically, and all scales demonstrated adequate reliability (Cronbach's α = 0.817–0.829; CR = 0.742–0.860; AVE = 0.602–0.769).

Implications for AI in Education

The study advances understanding of how higher education supports preparation for AI-intensive careers by clarifying the role of AI readiness as a key developmental mechanism. It argues that future research on AI and employability should move beyond treating digital competencies as direct predictors of career outcomes, instead examining the intermediate conditions that enable learning to become career-relevant. The differentiated roles of AI Literacy, self-efficacy, and readiness caution against aggregating them into undifferentiated skill indices. For management education, the findings call for curricular architectures that integrate AI literacy across courses and progression points, pedagogical designs that normalize iterative experimentation with AI tools under guided supervision, and Assessment strategies that evaluate applied judgment (problem framing, verification of outputs, responsible use) alongside technical understanding. It connects to Student Experience, Higher Ed, Equity, and Motivation, positioning AI readiness as a central construct for professional preparedness under continuous technological change.

Limitations

The study is cross-sectional, capturing AI literacy, readiness, and career adaptability at an early, anticipatory stage of professional development; it does not observe how these resources are enacted in stable organizational roles. The empirical setting is Italian higher education, so educational norms, labor-market expectations, and interpretations of AI may differ across national contexts. All data were collected via a single survey instrument (though common-method bias was mitigated). The sample focuses on economics/management/business students, and the authors note the model should be assessed across a broader spectrum of disciplines and cohorts, and extended to early-career professionals.

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Citation

Testa, M., Apuzzo, A., & Pittaway, L. (2026). AI literacy alone is not enough: Student AI readiness and career adaptability in business and management education. The International Journal of Management Education, 24, 101394.