BEYOND A SINGLE SCORE: A SUB-DIMENSIONAL ANALYSIS OF CAREER READINESS AMONG AI-ADOPTING UNIVERSITY STUDENTS

Abstract

Purpose – University career-readiness research typically treats the construct as a single holistic score, obscuring which specific competencies AI-adopting students report most and least strongly. This paper disaggregates career readiness into its five conceptual sub-dimensions to identify differential patterns and to lay the groundwork for future inferential testing.

Design/methodology/approach – Secondary descriptive analysis of item-level statistics (n = 331) from a dual-mediation study of AI adoption, academic success, and career readiness among Uzbekistani university students (Tukhtaeva & Mirza, 2026). The 15-item career readiness scale was disaggregated into its five underlying dimensions — professional orientation, knowledge application and problem-solving, communication and interpersonal skills, learning orientation and adaptability, and professional self-awareness — following the original study's own operationalisation (adapted from NACE, 2024; Portocarrero Ramos et al., 2025).

Findings – Communication and interpersonal skills and professional orientation scored highest among the five dimensions; professional self-awareness scored lowest. This lowest-scoring dimension is measured by a single item, and its low mean echoes a parallel gap identified in the same sample's critical-thinking construct, where the lowest-scoring item also concerned reflective self-evaluation.

Originality/value – To the authors' knowledge, this is the first sub-dimensional analysis of this dataset. It proposes a disaggregated structural model — explicitly anticipated as future work in the original study — as the next empirical step, contingent on access to respondent-level data.