The Industrial Education System under AI Conditions: Crisis of Individual Performance and Epistemistic Decoupling

This paper diagnoses the current educational phase as a structural transition in which an industrial education system encounters AI whose epistemic potential is incompatible with existing evaluation logics. The central issue is not the existence or use of AI, but the conditions under which it is used. A system primarily oriented toward reproducible, standardizable, and comparable outputs enforces a mode of AI usage that systematically suppresses its exploratory potential. The industrial education system is not fully reducible to reproduction. It contains areas of partial individual performance. However, these do not define the dominant system logic. Comparison, standardization, evaluability, and closure remain central. Under these conditions, AI is degraded to an answer machine. This logic is not directly imposed on AI by the system, but indirectly through learners, who are conditioned to use AI in a reproductive manner. The resulting transitional phase is characterized by epistemic decoupling: learners operate between factual practice and institutionally recognized practice. This creates a growing gap between actual learning and what continues to be recognized as legitimate. This decoupling undermines the epistemic authority of the system. At the same time, the crisis affects not only assessment formats but the concept of individual performance itself. The concept has been structurally overstretched, as reproducible and standardizable outputs were treated as indicators of individual performance. AI does not destroy individual performance, but reveals that reproducible performance was never identical with it. The focus of education therefore shifts toward orientation, exploration, and the ability to identify and structure relevance. A central phenomenon of this transition is epistemistic pseudo-orientation: AI-generated meaning produces coherence without grounded orientation. In extreme form, this can be described as a split condition in which learners operate within a new epistemic reality while being institutionally forced to maintain the fiction of the old system. What is often described as AI Shame reflects a deeper structural condition termed here the Transitional Legitimacy Gap: a situation in which a new practice is already widespread but not yet institutionally legitimate. This pattern is not historically new, but becomes intensified under AI conditions. AI also compresses cognitive processes and shifts thinking into pre-structured, coherent spaces. This compression is neither inherently an epistemic gain nor a loss, but it fundamentally alters the conditions of orientation. Orientation becomes dependent on the reconstructability of compression. Where reconstruction is absent, pseudo-orientation emerges. The existing education system is not prepared for this shift. The present situation should therefore not be understood as a technological disruption, but as a structural incompatibility between a system that continues to measure reproducible outputs and an epistemic reality in which such outputs have become machine-reproducible. Author keywords (free terms): Trackness; Incubativity; Delegability; TID triad; PreP; UnPreP; ICE-D; epistemic regulation; epistemic agency; Beyond Sectors.