Abstract

Artificial intelligence (AI) is increasingly entangled with teachers’ daily labor – from learning management systems and adaptive platforms to generative assistants and algorithmic evaluations. Drawing on Marx’s theory of alienation and labor process theory, this chapter examines how AI reconfigures teachers’ autonomy, authorship, practical wisdom, and relations with students and institutions. I develop an analytic framework that maps four forms of alienation (from product, process, others, and self) to common AI uses in schools and universities. Using illustrative vignettes from recent studies and policy cases, I show how AI can both intensify control through performance dashboards, scripted curricula, and automated surveillance, and enable craft restoration via planning support, differentiation at scale, and accessible feedback. To move beyond the binary of automation versus augmentation, I propose design and governance principles – transparency, contestability, situated consent, professional oversight, and teacher-led evaluation – that center pedagogical judgment. Overall, I argue that responsible AI in education is not simply a matter of technical accuracy or fairness but of protecting teachers’ capacity to care, create, and exercise discretion.