When AI Education Moves Faster Than Teaching Practice: Teacher Labour Alienation in the Transitional Phase of AI Integration

Abstract

Existing research on Artificial Intelligence in Education (AIED) has largely presupposed a mature landscape in which AI is fully embedded within schooling, whilst overlooking the initial transitional phase from conventional to AI-enabled instruction. Grounded in Marxist philosophy, this paper examines the central contradiction of this transitional period: namely, that social consciousness regarding the incorporation of AI into teaching has run considerably ahead of the material reality of instructional practice. From this disparity, three progressively intensified forms of alienation are derived: instructional performativity, wherein AI is reduced to a demonstration prop for open classes; the symbolic reification of AI, whereby it degenerates from a pedagogical aid into vacuous evaluative metrics; and reverse discipline over teachers by AI, such that quantifiable indicators come to serve as the primary measure of professional competence. Collectively, these three dimensions instantiate a Marxian cycle of alienation, characterised by a thoroughgoing estrangement from labour processes, labour products and species-being. The argument advanced here is that resolution of this crisis depends not upon further technological refinement, but rather upon restoring AI to its proper status as a productive force mediated by the historical agency of teachers themselves. Whilst the integration of AI into education presents a significant opportunity for pedagogical transformation, ultimate educational authority must remain vested in human practitioners. Focusing upon the alienation of teacher labour during this transitional moment, this study uncovers structural vulnerabilities latent within the AIED ecosystem via an analysis of these stratified alienated forms.