The Middle Space in Generative AI and Digital TVET Curriculum Design: A Competence Map Connecting Work, Learning, and Evidence

Generative artificial intelligence (GenAI) is simultaneously reconfiguring occupational task allocation and the evidentiary conditions under which competence is judged in technical and vocational education and training (TVET). By generating text, explanations, and recommendations, it lowers the cost of producing fluent outputs while weakening the extent to which final products alone demonstrate learners’ independent capabilities. This article uses an integrative conceptual synthesis to connect research on vocational competence, digital competence, digital work, constructive alignment, boundary objects, and evidence-centered assessment. It develops the argument that responsible digital mediation should be treated as a transversal core capability: learners must decide when to use, constrain, or reject generative tools on the basis of task fit, comparative advantage, verification feasibility, and protection of competence evidence, while retaining responsibility for outcomes. The article positions the competence map as a secondary, intermediary translation mechanism in the curriculum-making middle space. In a minimal sufficient form, the map links occupational tasks, competence claims, learning outcomes, activities, tool boundaries, and assessment evidence without replacing existing occupational standards, competency frameworks, curriculum maps, or rubrics. An illustrative Smart Warehousing and Data Operations prototype shows that deterministic systems remain preferable for structured calculation and transaction processing, whereas GenAI can expand the option space in verifiable episodes of hypothesis generation, interpretation of unstructured information, and audience-sensitive communication. The contribution is conceptual rather than causal: it reframes GenAI as a dual disruption of work and evidence and specifies conditions under which curriculum use is educationally defensible and administratively proportionate.