Conference Program

Current debates on artificial intelligence in education are largely framed through instrumental and functional perspectives, emphasising efficiency, personalisation, learning analytics, or pedagogical support. In higher education, these approaches frequently invoke a democratic promise of AI, associated with widened and inclusive access to academic resources, support for student success, and the mitigation of educational inequalities. However, they often leave unexamined the deeper transformations that AI introduces into the constitution of academic subjectivity and into the symbolic foundations of educational democracy.
This paper proposes a critical and theoretically grounded analysis of AI in higher education, not as a neutral tool, but as a new grammar of educational power. Drawing on contemporary reflections on post-humanity—particularly those developed by Yuval Noah Harari—the paper engages with diagnoses of the progressive delegation of judgement to non-conscious systems, the fragilisation of the modern autonomous subject, and the growing role of algorithms as mediators of reality, normativity and truth. Rather than adopting these perspectives uncritically, the paper relocates them within educational institutions, asking what they imply when applied to concrete practices of teaching, learning and evaluation.
The analysis conceptualises the emergence of a post-human academic subjectivity affecting both university teachers and students, as judgement, evaluation and academic legitimacy become increasingly co-constructed through interactions with AI systems, particularly in assessment contexts. The aim is not to offer a binary axiological assessment of AI use, but to document the emergence of a hybrid form of subjectivity, reshaped in its relation to knowledge, learning and academic norms through algorithmic mediation. The argument is informed by illustrative material drawn from an exploratory qualitative enquiry focusing on students’ reported uses of AI in their everyday academic work, especially during evaluative tasks. These accounts highlight recurrent practices of anticipatory adjustment, norm alignment and risk avoidance, through which AI is mobilised to secure conformity to perceived academic expectations rather than to support the assumption of a personal intellectual position.
From an educational and clinical perspective, such uses cannot be reduced to gains in efficiency or access. They point to a reconfiguration of the subject’s relation to knowledge, judgement and responsibility, and to a displacement of educational injustice: while certain barriers to access may be lowered, new forms of symbolic dispossession emerge, as the capacity to assume uncertainty, disagreement and accountability is partially delegated to algorithmic mediation.
The paper advances three main arguments. First, it argues that so-called “reasonable uses of AI” in higher education are not merely a technical or ethical issue, but a symbolic and political one, reshaping how academic subjects relate to judgement and normativity. Second, it contends that educational democracy presupposes subjects capable of assuming judgement and sustaining disagreement—conditions that conformity-oriented uses of AI tend to neutralise. Third, it proposes a situated analytical framework combining democratic theory, critical philosophy and a clinical sensitivity to subjectivity, without collapsing into either technophobia or technosolutionism.