Conference Program

The rapid uptake of generative AI—especially Transformer-based Large Language Models (LLMs; Vaswani et al., 2017)—is both a symptom and a catalyst of ongoing attempts at a neoliberal restructuring of the university (Slaughter & Rhoades, 2004). Writing, assessment, and feedback are increasingly recast as measurable textual performances aligned with ranking logics, accountability frameworks, and the promise of a high-return educational investment, while students are treated as consumers who purchase marketable skills (Selwyn, 2016).

This paper introduces the Mirror-Box Bias as both an epistemological and an institutional device. Just as a mirror-box patient perceives a phantom limb by projecting the reflection of the healthy arm, educators and evaluators project human cognition as the sole legitimate form of intelligence and treat it as a normative gold standard for assessing any other form of cognition (Ramachandran & Rogers-Ramachandran, 1996). This projective mechanism generates two symmetrical and interconnected distortions: an illusion of artificial limitation, whereby the non-anthropomorphic architecture of LLMs is read as cognitive deficit simply because it does not mirror human processes; and an illusion of artificial comprehension, whereby conversational coherence is mistaken for genuine understanding. In both cases, difference is reduced to deficiency and the Protagorean dictum that “man is the measure of all things” is uncritically extended to AI evaluation. Because both illusions restrict the field of what counts as “intelligence” to what is textually producible and specularly comparable between human and machine, they converge with an efficiency-driven governance that rewards measurable output: everything that resists textual reduction—critical thinking, reflective practice, bodily knowledge, relational vulnerability—is pushed to the margins of assessment and, consequently, of curricula. The Mirror-Box Bias thus naturalises the reduction of learning to performance and marginalises precisely those forms of understanding that the university should safeguard (Biesta, 2017).

Drawing on the Platonic–Derridean pharmakon (Derrida, 1972), embodied cognition (Varela et al., 1991), and critical pedagogy (Freire, 1970; Giroux, 1983), the paper offers a critical genealogy of “the human” in the age of LLMs and positions itself beyond the dichotomy of technophobia and technophilia. It advances a post-anthropocentric pedagogy (Braidotti, 2013) that does not abandon the human dimension of education but integrates it within a broader framework recognising the complementarity of diverse cognitive architectures—human and artificial. Three principles are proposed: (1) AI as a non-substitutive interlocutor and decentring device that reopens, rather than forecloses, the space for critical thinking; (2) process-oriented assessment grounded in attribution, transparency, and responsibility, as an alternative to output measurement; (3) re-anchoring learning in situated, bodily, and communal practices that resist the commodification of education. The framework directly addresses the tension between institutional survival and ethical mission by reclaiming the university as a space of academic freedom, equity, and public responsibility.