Journal of East China Normal University(Educationa ›› 2026, Vol. 44 ›› Issue (9): 70-84.doi: 10.16382/j.cnki.1000-5560.2026.09.007

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How Education Responds to the Representational Crisis of Generative AI

Chengyu Yang   

  1. Institute of Curriculum and Instruction, East China Normal University, Shanghai 200062, China
  • Online:2026-09-01 Published:2026-08-29

Abstract:

The explosion of generative artificial intelligence (GenAI) marks a fundamental rupture in the human cognitive ecology, driving education through a profound transition from a “retrieval paradigm” to a “generative paradigm.” Relying on probabilistic computation and the deep semantic simulation of natural language, GenAI has substantially taken over the power of representational production at the ontological level. This deep externalization of representation, coupled with the algorithmic mediation of educational practices, will lead to a systemic imbalance in life perception, meaning comprehension, emotional connection, and subject construction within the educational field. Consequently, this tears open deep chasms while simultaneously implying the potential for de-anthropocentrism. Faced with this “crisis,” both traditional representationalism and naive non-representational theories reveal a poverty of explanatory power. Based on Gilles Deleuze’s philosophy of “differential becoming” and Jacques Derrida’s theory of “différance,” this paper constructs a theoretical framework of “generative intertwining,” demarcating the boundary between the “probabilistic repetition of algorithms” and the “differential becoming of life.” In the dialectic of alignment and flight, education does not reject representation and AI; rather, it integrates machine-generated generality as its underlying substrate. Furthermore, as a technology of différance, education should respond to the gap of understanding by using differential narratives to defer judgments of meaning; bridge the gap of life by recalibrating sensory anchors to extend bodily judgment; traverse the gap of emotion by activating affective generation through aesthetic encounters in aesthetic education; and ultimately respond to the gap of subjectivity by reforming evaluation mechanisms to regather the responsibility dispersed by algorithms and reconstruct the nomadic subject.

Key words: generative artificial intelligence, representation, non-representation, externalization of representation, nomadic subject