This paper investigates graduate skill gaps from a work-based learning (WBL) perspective by examining triadic differences in how students, lecturers and employers perceive employability skills in an applied university context.
A convergent mixed-methods design was employed, integrating survey data from 537 stakeholders (341 students, 102 lecturers and 94 employers) with thematic analysis of qualitative responses.
Significant misalignments exist: lecturers rated students' technical skills highest, while students overestimated their soft skills. While quantitative data showed no significant gap in digital skills, qualitative insights revealed a “digital-readiness paradox” where employers expressed deep concerns about graduates' ability to handle data-driven and AI-integrated workflows.
The study is limited to a single applied institution. Future research should involve longitudinal tracking of graduates to measure the actual performance-perception gap.
Universities must shift from generic digital literacy to “AI-readiness” and strengthen WBL through industry-led simulations and authentic assessments.
This study advances the employability literature by adopting a unique triadic analytical framework to contrast the perceptions of students, lecturers and employers. While prior research (e.g. Nguyen, 2024) has focused on individual agency, this paper unearths the “digital-AI readiness gap” – a latent misalignment in data-driven workflows often missed by standard surveys. By integrating mixed-methods data within an emerging Southeast Asian context, the study provides a strategic roadmap for recalibrating work-based learning to meet the shifting technological demands of the 2025–2030 labor market.
