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Latent Profile Analysis of AI Readiness, Digital Utilization at work, and Soft Skills Among University Graduates: Associations with Job Satisfaction and Well-Being

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(4), pp.257~266
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 26, 2026
  • Accepted : March 31, 2026
  • Published : April 30, 2026

Yeong Lee 1 Tami Im 2

1동국대학교
2국립공주대학교

Accredited

ABSTRACT

The purpose of this study is to classify latent groups of university graduates based on their AI readiness, level of digital utilization, and possession of soft skills, and to examine differences in job satisfaction and well-being across these groups. Using data from the 8th-year student panel (2024) of the Korea Employment Education Panel II (KEEP II), latent profile analysis and the BCH method were employed. First, four distinct classes emerged: the High digital utilization-Mid AI readiness-Low soft skills class, the High AI readiness·digital utilization-Low soft skills class, the High soft skills-Low AI readiness·digital utilization class, and the High AI readiness-Mid soft skills-Low digital utilization class. Second, job satisfaction levels were highest in the High AI readiness·digital utilization-Low soft skills class, while the High digital utilization-Mid AI readiness-Low soft skills class reported the lowest job satisfaction. Third, in terms of well-being, the High AI readiness-Mid soft skills-Low digital utilization class scored the highest, and the High soft skills-Low AI readiness·digital utilization class scored the lowest. Based on these findings, tailored educational strategies were proposed for each latent class. This study contributes by providing foundational data for developing tailored educational and human resource development strategies based on latent group characteristics, through a multidimensional analysis of university graduates’ competency structures.

Citation status

* References for papers published after 2024 are currently being built.

This paper was written with support from the National Research Foundation of Korea.