This study critically investigates the ethical and structural implications of artificial intelligence (AI) integration into higher education through Karl Marx’s theory of alienation. Drawing upon empirical data from a survey of 395 educators in Northern Cyprus, an illustrative context characterised by nascent AI adoption, the research identifies significant experiences of alienation among educators arising from AI-driven transformations of academic labour. Alienation from the product of academic labour, marked by diminished sense of ownership and authorship over teaching outcomes, emerged as particularly pronounced. Further, alienation from educational processes, professional identity, and interpersonal relations also surfaced prominently, highlighting the multifaceted pressures imposed by algorithmic management practices and standardisation in higher education settings. Positive perceptions of AI were significantly correlated with reduced alienation across all dimensions, although such optimism alone could not eliminate structural tensions inherent in AI implementation. The nuanced and fragmented nature of educators' attitudes toward AI underscores the necessity for nuanced, context-sensitive governance approaches. The findings underscore the urgent need for higher education institutions to adopt ethical governance frameworks that prioritise transparency, participatory decision-making, professional autonomy, and robust ethical oversight. In doing so, universities can better harness AI’s educational potential while preserving the professional integrity, creative agency, and relational ethics at the heart of academic practice.

KEYWORDS: AI in education, teacher alienation, Marxist theory, educational technology, labour autonomy

This article critically examines the integration of artificial intelligence (AI) in education through the dual lenses of sustainability and ethics, grounded in a Marxian-inspired analysis of academic labour. Drawing on a survey of 395 educators in Northern Cyprus, the study finds that AI-driven transformations in teaching are accompanied by significant experiences of alienation among educators. Four interrelated dimensions of alienation are identified: (1) Alienation from the product of academic labour, (2) Alienation from the educational process, (3) Alienation from professional identity (species-being), and (4) Alienation from interpersonal relations. Notably, educators with positive perceptions of AI experienced significantly lower alienation across all dimensions, suggesting that when AI is perceived as a supportive tool aligned with educators’ values, its integration is more sustainable. However, optimism alone cannot resolve the structural tensions inherent in AI implementation. These findings underscore the need for educational institutions to adopt ethically grounded, sustainable AI governance frameworks that prioritise transparency, human oversight, participatory decision-making, and professional autonomy.