From generative AI literacy to perceived employability: technological self-efficacy, career adaptability and internship quality in Taiwan's TVET
Stanley Y.B. HuangPurpose
This study examines how perceived generative AI literacy (GAIL) becomes employability-relevant for technical and vocational education and training (TVET) students through technological self-efficacy and career adaptability, and whether internship quality strengthens this pathway.
Design/methodology/approach
526 Taiwanese TVET students with internship experience was analysed using standardised composite-score path analysis, 5,000-resample nonparametric bootstrapping, moderation tests and subgroup diagnostics.
Findings
All six hypotheses were supported. Perceived GAIL was positively associated with technological self-efficacy, career adaptability and perceived employability through the hypothesised serial pathway. Internship quality strengthened the first-stage association. Supplementary records linked perceived GAIL with task performance and supervisor ratings, and perceived employability with later employment and job fit.
Research limitations/implications
Although supplementary performance, rater, supervisor and follow-up records reduce sole dependence on self-report, the primary structural model remains observational and perception-based. Future research should pre-register performance assessments and test measurement invariance across TVET tracks.
Practical implications
TVET providers should teach GenAI literacy as responsible task performance, not mere tool use, and connect it to supervised internship tasks, feedback and transparent skill signalling.
Originality/value
The study specifies a GenAI-focused, theory-grounded employability pathway and identifies internship quality as a workplace-learning condition under which perceived digital competence is more strongly associated with career confidence.