AI-Driven Career Guidance to Reduce Vocational Students’ Career Path Anxiety through Skills Mapping, Adaptive Mentoring, and Labor Market Intelligence

Vocational students often experience career path anxiety due to uncertainty about labor market demands, limited mentoring, and misalignment between curricula and industry needs. In Indonesia, this is amplified by uneven career guidance despite mandates for workforce readiness. Recent advances in artificial intelligence (AI) enable adaptive, data-driven, and psychologically informed support that links students’ skills with real-time labor markets. This study used a design science research approach to build and evaluate an AI-driven career guidance system with three components: (1) a supervised machine learning skills mapping engine, (2) an adaptive mentoring module using an AI chatbot and mentor matching, and (3) a real-time labor market intelligence module using natural language processing to analyze job postings and trends. A mixed-methods evaluation involved 180 vocational students from three schools in South Kalimantan assigned to intervention and control groups. Quantitative data were collected through pre–post career anxiety surveys and system performance metrics, while qualitative data were gathered through interviews and focus group discussions. Analysis included paired-sample t-tests, predictive model evaluation, and thematic analysis. Students using the AI system showed a significant 26.7% reduction in career path anxiety compared with minimal change in the control group (p