AI-Based Adaptive Career Guidance for Sustainable Transformation in TVET Education

Abstract

The transformation of vocational education requires career guidance models that are personalized, adaptive, and capable of responding to rapidly evolving labor market expectations. Existing guidance practices in vocational institutions remain largely general, static, and fragmented, providing limited support for students’ career development and lacking integration of psychological, academic, and contextual data. This study aims to identify the needs of vocational learners and key stakeholders and to develop a conceptual framework for an artificial intelligence-based adaptive career guidance system grounded in established career development theories. Using a Design and Development Research approach, data were collected through surveys with 210 students and through interviews and focus group discussions with guidance counselors and industry representatives. The findings indicate a strong demand for individualized, technology-supported guidance, while counselors and industry partners highlighted challenges related to data fragmentation, limited digital tools, and misalignment between student competencies and workforce requirements. Based on these insights, the study proposes a conceptual framework that integrates multimodal data processing, natural language analysis, psychometric profiling, and adaptive recommendation mechanisms. The framework provides a theoretically coherent and contextually relevant foundation for developing intelligent guidance systems that support more responsive, ethical, and sustainable career development in vocational education.