The Influence Of Learning Experience In Intelligent Systems Concentration On Computer Engineering Students’ Work Readiness In Artificial Intelligence With Self-Efficacy As A Mediating Variable
TL;DR
Findings indicate that self-efficacy partially mediates the relationship between learning experience and work readiness, and Meaningful AI-related learning experiences therefore strengthen both students’ competencies and confidence in preparing for AI-related careers.
Abstract
This study examines the influence of learning experiences in the Intelligent Systems concentration on Computer Engineering students’ work readiness in Artificial Intelligence (AI), with self-efficacy as a mediating variable. A quantitative approach with an explanatory research design was used. The participants were 40 Computer Engineering students from the 2022 cohort in the Intelligent Systems concentration, selected through purposive sampling. Data were collected using a four-point Likert-scale questionnaire measuring learning experience, self-efficacy, and work readiness. The instruments were tested for validity and reliability, and the data were analyzed using descriptive statistics and bootstrap-based mediation analysis with PROCESS Macro Model 4 in IBM SPSS Statistics 26. The results showed that learning experience had a positive and significant effect on self-efficacy, self-efficacy had a positive and significant effect on work readiness, and learning experience had a significant direct effect on work readiness. The indirect effect was also significant, with a coefficient of 0.8254 and a 95% bootstrap confidence interval of 0.4518 to 1.2224. These findings indicate that self-efficacy partially mediates the relationship between learning experience and work readiness. Meaningful AI-related learning experiences therefore strengthen both students’ competencies and confidence in preparing for AI-related careers.