AI Use, Environmental Awareness and Moral Disengagement Among Gen Z Business Students

Abstract

Generative artificial intelligence (AI) has become a defining feature of how Generation Z business students learn, work, and produce knowledge. It is also among the most resource-intensive consumer technologies ever deployed, requiring substantial water and electricity for both training and use. Yet the environmental costs of AI remain largely invisible to its end users, and existing AI literacy frameworks systematically exclude environmental considerations from their account of responsible AI use. Drawing on four focus groups and 42 open-response questionnaires conducted at an international business school, this study examines how environmentally aware Gen Z business students reconcile intensive AI use with their stated environmental commitments. Using reflexive thematic analysis of 395 double-coded segments, we identify a four-stage moral disengagement architecture for AI sustainability. The architecture begins with productivity primacy with AI use framed as professionally unavoidable, proceeds through rationalisation and collective disengagement, then responsibility delegation to governments and corporations, and culminates in technological solutionism, in which future innovation is expected to resolve environmental concerns. Each stage closes a specific cognitive and moral gap left open by the previous one, producing an internally coherent structure that allows pro-environmental values to coexist with high-intensity AI use without moral incongruence. The findings also reveal asymmetry in AI education. Participants demonstrated awareness of cognitive and academic risks associated with AI, largely reflecting institutional messaging, while environmental risks remained fragmented, informal, and weakly connected to behavioural decision-making. Although many students expressed concern about sustainability, environmental considerations rarely entered their ethical evaluation of AI use. The study extends Bandura's moral disengagement theory by demonstrating its sequential, architecture-like operation in a high-uptake technology context. It also proposes Green AI Literacy as a missing dimension of existing AI literacy frameworks. Finally, it provides preliminary insights into how higher education institutions might better integrate environmental sustainability into AI literacy education.