When AI Ethics Shapes Motivation and Trust: Toward Sustainable AI Adoption in Education

Mouna Allouche, Molka Boujelben

AI integration in higher education is currently hindered by "algorithmic anxiety" and ethical friction. Addressing the dominant output-centric focus, this study examines how perceived ethics, trust, and motivation jointly foster academic well-being as the essential catalyst for sustainable AI adoption. We propose an integrative framework extending the Smart Technology Acceptance Model (STAM) with Trust Theory and Self-Determination Theory (SDT). PLS-SEM analysis ( n=100) validates a robust "Morality-to-Well-being" pathway, positioning perceived ethics as the axial antecedent of systemic trust, which catalyzes intrinsic motivation. Findings confirm a full sequential mediation: moral perceptions are internalized via "psychological sedimentation," transforming ethical compliance into enduring academic well-being. This framework facilitates a paradigm shift from technocentric efficiency to human-centric flourishing, repositioning ethics not as a constraint but as a strategic lever for institutional legitimacy and pedagogical health.