Vocational students often experience high career decision anxiety due to uncertainty about labor market demands, limited mentoring, and gaps between school curricula and industry needs. This issue is critical in Indonesia, where vocational education plays a key role in national human capital development. This study used a mixed-methods design involving 320 vocational students and developed an AI-driven career guidance system integrating three modules: a supervised machine learning–based skills mapping engine, an adaptive mentoring module with an AI chatbot and mentor matching, and a labor market intelligence component using natural language processing to analyze job postings. Data were collected through pre- and post-intervention surveys, system analytics, and interviews. Quantitative analysis used descriptive statistics, paired t-tests, and regression modeling, while qualitative data were analyzed thematically. Results showed a 35% reduction in career anxiety after eight weeks of system use. The skills mapping module achieved 87% matching accuracy, the mentoring module supported over 2,400 chatbot interactions, and 78% of students reported increased confidence in career planning. Labor market analytics processed 500 job postings to provide real-time insights. The findings confirm that the AI-driven system effectively reduces career anxiety and improves vocational students’ career readiness.
andryananda. et al. (Tue,) studied this question.