Ethical and Responsible AI in Education: Situated Ethics for Democratic Learning

As AI systems increasingly structure educational processes, they shape not only what is learned, but also how epistemic authority is distributed and whose knowledge is recognized. This article explores the normative and technopolitical implications of this development by examining two prominent paradigms in AI ethics: Ethical AI and Responsible AI. Although often treated as synonymous, these frameworks reflect distinct tensions between formal universalism and contextual responsiveness, between rule-based evaluation and governance-oriented design. Drawing on deontology, utilitarianism, responsibility ethics, contract theory, and the capability approach, the article analyzes the frictions that emerge when these frameworks are applied to algorithmically mediated education. The argument situates these tensions within broader philosophical debates on technological mediation, normative infrastructures, and the ethics of sociotechnical design. Through empirical examples such as algorithmic grading and AI-mediated admissions, the article shows how predictive systems embed values into optimization routines, thereby reshaping educational space and interpretive agency. In response, it develops the concept of situated ethics, emphasizing epistemic justice, learner autonomy, and democratic judgment as central criteria for evaluating educational AI. To clarify what is at stake, the article distinguishes adaptive learning optimization from education as a process of subject formation and democratic teaching objectives. Rather than viewing AI as an external tool, the article conceptualizes it as a co-constitutive actor within pedagogical practice. Ethical reflection must therefore be integrated into design, implementation, and institutional contexts from the outset. Accordingly, the article offers (1) a conceptual map of ethical paradigms, (2) a criteria-based evaluative lens, and (3) a practice-oriented diagnostic framework to guide situated ethics in educational AI. The paper ultimately argues for an approach that attends to the relational, political, and epistemic dimensions of AI systems in education.