How can education systems develop the competences required for effective human–AI collaboration while simultaneously strengthening connections between vocational education, higher education, and industry? This paper addresses this question by analysing emerging competence requirements and institutional responses in European higher education and higher vocational education and training (HVET) contexts.

The study follows a mixed-methods research design combining a structured literature review on future skills and AI-related competencies, a European-wide survey (n = 150), and semi-structured expert interviews conducted across five countries. The analysis focuses on identifying competence gaps and structural barriers in current education and training systems, particularly in relation to research and innovation capacity.

The findings result in an empirically grounded competence framework (TRIComp), which structures 22 key competencies across domains such as digital transformation, innovation, and interdisciplinary collaboration. The results indicate a mismatch between existing training provision and emerging competence demands, especially regarding AI literacy, applied research skills, and the ability to operate in complex cross-sector environments. In addition, the findings highlight the growing importance of self-directed learning and the evolving role of educators as facilitators of inquiry-based and innovation-oriented learning processes.

Building on these insights, the paper outlines an emerging institutional model for cross-sector education ecosystems, conceptualised as a “Technological Village.” The study contributes to current debates on institutional transformation and capacity-building by linking empirical evidence on future skill demands with a scalable model for education ecosystem design.