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.

Higher education (HE) has become a central site where the relations between democracy, pedagogy and technology are being reshaped through algorithmic infrastructures. In this context, a specific tension becomes visible: as educational processes become intertwined with systems of classification, prediction and optimization, recognition risks becoming conditional on data legibility, while pedagogical judgement is redirected toward procedural efficiency. Against this background, this article investigates how subjectivity, recognition and pedagogical responsibility can be conceptually framed when formative encounters are mediated through pedagogical practice as well as through algorithmic operations. To address this question, it develops the DEA model (Democratic Education under Algorithmic Conditions) as a reflexive, education–theoretical heuristic grounded in educational theory, subjectivation research, democratic thought and critical data studies. The model positions education, democracy and digitalisation as interdependent fields and specifies three analytical dimensions: formative, normative and inferential. These are elaborated through relational vectors and framing structures that include societal discourses, institutional configurations, cultural imaginaries and biographical conditions. The reconstruction shows how pedagogical responsibility becomes vulnerable to displacement by optimization routines, how recognition is reorganised by regimes of data legibility and how didactic relations are reconfigured through automated feedback and recommendation systems. Rather than prescribing technical solutions, the DEA model offers a conceptual orientation for tracing how algorithmic mediation redistributes recognition, responsibility and legitimacy in HE, and for sustaining Bildung and democratic subject formation under digital conditions.

Generative artificial intelligence (AI) is reshaping higher education (HE) by reconfiguring how knowledge becomes visible, how judgment is exercised, and how recognition is distributed. These systems intervene in the pedagogical and democratic conditions under which plurality, critique, and participation can be sustained. This study examines how students and lecturers interpret and navigate these transformations and what they reveal about the possibilities of democratic education under algorithmic mediation. Drawing on n = 151 written articulations (122 students, 29 lecturers) to open-ended questions collected via LimeSurvey, analyzed through Grounded Theory in combination with biographical interpretation and oriented by education theory (Bildung) and democracy pedagogy, the research reconstructs five orientations that range from pragmatic coping to struggles over recognition. These orientations illuminate how systemic dynamics of acceleration, opacity, and infrastructural authority are refracted into everyday academic practice. They are further synthesized into three broader axes of temporal sovereignty, epistemic opacity and accountability, and recognition ecologies. The findings highlight how fragile orientations emerge as both risks and resources. The study contributes to HE didactics by outlining strategies to transform fragility into pedagogical occasions, emphasizing reflective delay, dialogical engagement with opacity, and diversification of recognition practices. It concludes that democratic education depends on cultivating spaces where algorithmic pressures become educable and fragile orientations can develop into dispositions of reflexivity, critique, and participation.