Teaching AI Microethics in Higher Education

Abstract

The growing ubiquity of artificial intelligence (AI) in critical sociotechnical systems imposes an inescapable responsibility on engineers, who make design decisions with profound ethical implications at the micro level. This article presents a critical review of the literature on teaching AI microethics in engineering education programs. Through a systematic analysis of academic publications, institutional guidelines, and corporate ethical frameworks, a significant gap is evidenced between the emerging consensus on key ethical principles—such as fairness, transparency, explainability, and accountability—and their effective pedagogical implementation in engineering curricula. The results reveal a lack of concrete educational strategies that transcend theoretical discussion and equip future engineers with practical competencies to address ethical dilemmas throughout the AI development lifecycle. It is concluded that it is imperative to reformulate curricula to incorporate applied ethics not as a supplement, but as a fundamental pillar of engineering education. This involves adopting responsible-by-design methodologies, the use of case studies, ethical impact matrices, and bias audit tools, to cultivate the critical judgment and moral sensitivity necessary for the development of trustworthy and human-centered AI.